[deps] Pin transformers to 4.47

[chore] Setup deps for doc build
[chore] Update
2025-04-21 12:24:03 +00:00 · 2025-04-21 12:24:00 +00:00 · 2025-04-21 12:24:00 +00:00 · 2025-04-21 12:23:56 +00:00 · 2025-04-21 08:38:06 +00:00 · 2025-04-21 08:21:16 +00:00
152 changed files with 13951 additions and 125911 deletions
--- a/.github/workflows/deploy-doc.yml
+++ b/.github/workflows/deploy-doc.yml
@@ -0,0 +1,31 @@
+name: "Sphinx: Render docs"
+
+on: push
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+    - name: Build HTML
+      uses: ammaraskar/sphinx-action@7.0.0
+      with:
+        pre-build-command: |
+          apt-get update && apt-get install -y git
+          pip install uv
+          uv pip install --system . .[docs]
+    - name: Upload artifacts
+      uses: actions/upload-artifact@v4
+      with:
+        name: html-docs
+        path: docs/build/html/
+    - name: Deploy
+      uses: peaceiris/actions-gh-pages@v3
+      if: github.ref == 'refs/heads/main'
+      with:
+        github_token: ${{ secrets.GITHUB_TOKEN }}
+        publish_dir: docs/build/html
--- a/.github/workflows/pr-welcome.yml
+++ b/.github/workflows/pr-welcome.yml
@@ -0,0 +1,41 @@
+name: PR Welcome Bot
+
+on:
+  pull_request:
+    types: [opened]
+
+permissions:
+  pull-requests: write
+  issues: write
+
+jobs:
+  welcome:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Post Welcome Comment
+        uses: actions/github-script@v6
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            const prNumber = context.issue.number;
+            const prAuthor = context.payload.pull_request.user.login;
+
+            const welcomeMessage = `
+            👋 Hello @${prAuthor}, thank you for contributing to this project! 🎉
+
+            We've received your Pull Request and the team will review it as soon as possible.
+
+            In the meantime, please ensure:
+            - [ ] Your code follows the project's coding style
+            - [ ] Relevant tests have been added and are passing
+            - [ ] Documentation has been updated if needed
+
+            If you have any questions, feel free to ask here. Happy coding! 😊
+            `;
+
+            await github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: prNumber,
+              body: welcomeMessage
+            });
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,34 @@
+name: Publish to PyPI
+
+on:
+  push:
+    tags:
+      - 'v*'
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.x'
+
+    - name: Install uv
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.cargo/bin" >> $GITHUB_PATH
+
+    - name: Build package with uv
+      run: |
+        uv build
+
+    - name: Publish to PyPI
+      env:
+        UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }}
+      run: |
+        uv publish --token $UV_PUBLISH_TOKEN
--- a/.github/workflows/python-lint.yml
+++ b/.github/workflows/python-lint.yml
@@ -0,0 +1,27 @@
+name: Python Linting
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+          cache: 'pip'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pre-commit
+
+      - name: Run pre-commit
+        run: pre-commit run --all-files
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -0,0 +1,35 @@
+name: Run Tests with Pytest
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.x'
+
+    - name: Install uv
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.cargo/bin" >> $GITHUB_PATH
+
+    - name: Install dependencies
+      run: |
+        uv sync --extra test
+
+    - name: Run tests with pytest
+      run: |
+        uv run pytest -v tests/
--- a/.gitignore
+++ b/.gitignore
@@ -1,10 +1,349 @@
-**/__pycache__
-**/.vscode
-**/train_result
+# Created by https://www.toptal.com/developers/gitignore/api/macos,visualstudiocode,pycharm,python
+# Edit at https://www.toptal.com/developers/gitignore?templates=macos,visualstudiocode,pycharm,python

-**/logs
-**/.cache
-**/tmp*
-**/data
-**/*cache
-**/ckpt
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### macOS Patch ###
+# iCloud generated files
+*.icloud
+
+### PyCharm ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### PyCharm Patch ###
+# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721
+
+# *.iml
+# modules.xml
+# .idea/misc.xml
+# *.ipr
+
+# Sonarlint plugin
+# https://plugins.jetbrains.com/plugin/7973-sonarlint
+.idea/**/sonarlint/
+
+# SonarQube Plugin
+# https://plugins.jetbrains.com/plugin/7238-sonarqube-community-plugin
+.idea/**/sonarIssues.xml
+
+# Markdown Navigator plugin
+# https://plugins.jetbrains.com/plugin/7896-markdown-navigator-enhanced
+.idea/**/markdown-navigator.xml
+.idea/**/markdown-navigator-enh.xml
+.idea/**/markdown-navigator/
+
+# Cache file creation bug
+# See https://youtrack.jetbrains.com/issue/JBR-2257
+.idea/$CACHE_FILE$
+
+# CodeStream plugin
+# https://plugins.jetbrains.com/plugin/12206-codestream
+.idea/codestream.xml
+
+# Azure Toolkit for IntelliJ plugin
+# https://plugins.jetbrains.com/plugin/8053-azure-toolkit-for-intellij
+.idea/**/azureSettings.xml
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json
+
+### VisualStudioCode ###
+**/.vscode
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Local History for Visual Studio Code
+.history/
+
+# Built Visual Studio Code Extensions
+*.vsix
+
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
+.ionide
+
+# End of https://www.toptal.com/developers/gitignore/api/macos,visualstudiocode,pycharm,python
+
+uv.lock
+**/train_result
+**/*.onnx
+**/*.png
+**/*.jpg
+**/augraphy_cache
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,22 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.11.6
+    hooks:
+      - id: ruff
+        args: [--fix, --respect-gitignore, --config=pyproject.toml]
+        exclude: ^texteller/models/thrid_party/paddleocr/
+      - id: ruff-format
+        args: [--config=pyproject.toml]
+        exclude: ^texteller/models/thrid_party/paddleocr/
+
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-toml
+      - id: check-added-large-files
+      - id: check-case-conflict
+      - id: check-merge-conflict
+      - id: debug-statements
--- a/.python-version
+++ b/.python-version
@@ -0,0 +1 @@
+3.10
--- a/202
+++ b/202
@@ -0,0 +1,202 @@
+
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+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
--- a/README.md
+++ b/README.md
@@ -0,0 +1,215 @@
+📄 English | <a href="./assets/README_zh.md">中文</a>
+
+<div align="center">
+    <h1>
+        <img src="./assets/fire.svg" width=30, height=30>
+        𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛
+        <img src="./assets/fire.svg" width=30, height=30>
+    </h1>
+
+  [![](https://img.shields.io/badge/API-Docs-orange.svg?logo=read-the-docs)](https://oleehyo.github.io/TexTeller/)
+  [![](https://img.shields.io/badge/docker-pull-green.svg?logo=docker)](https://hub.docker.com/r/oleehyo/texteller)
+  [![](https://img.shields.io/badge/Data-Texteller1.0-brightgreen.svg?logo=huggingface)](https://huggingface.co/datasets/OleehyO/latex-formulas)
+  [![](https://img.shields.io/badge/Weights-Texteller3.0-yellow.svg?logo=huggingface)](https://huggingface.co/OleehyO/TexTeller)
+  [![](https://img.shields.io/badge/License-Apache_2.0-blue.svg?logo=github)](https://opensource.org/licenses/Apache-2.0)
+
+</div>
+
+https://github.com/OleehyO/TexTeller/assets/56267907/532d1471-a72e-4960-9677-ec6c19db289f
+
+TexTeller is an end-to-end formula recognition model, capable of converting images into corresponding LaTeX formulas.
+
+TexTeller was trained with **80M image-formula pairs** (previous dataset can be obtained [here](https://huggingface.co/datasets/OleehyO/latex-formulas)), compared to [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) which used a 100K dataset, TexTeller has **stronger generalization abilities** and **higher accuracy**, covering most use cases.
+
+>[!NOTE]
+> If you would like to provide feedback or suggestions for this project, feel free to start a discussion in the [Discussions section](https://github.com/OleehyO/TexTeller/discussions).
+
+---
+
+<table>
+<tr>
+<td>
+
+## 🔖 Table of Contents
+- [Getting Started](#-getting-started)
+- [Web Demo](#-web-demo)
+- [Server](#-server)
+- [Python API](#-python-api)
+- [Formula Detection](#-formula-detection)
+- [Training](#️️-training)
+
+</td>
+<td>
+
+<div align="center">
+  <figure>
+    <img src="assets/cover.png" width="800">
+    <figcaption>
+      <p>Images that can be recognized by TexTeller</p>
+    </figcaption>
+  </figure>
+  <div>
+  </div>
+</div>
+
+</td>
+</tr>
+</table>
+
+## 📮 Change Log
+
+- [2024-06-06] **TexTeller3.0 released!** The training data has been increased to **80M** (**10x more than** TexTeller2.0 and also improved in data diversity). TexTeller3.0's new features:
+
+  - Support scanned image, handwritten formulas, English(Chinese) mixed formulas.
+
+  - OCR abilities in both Chinese and English for printed images.
+
+- [2024-05-02] Support **paragraph recognition**.
+
+- [2024-04-12] **Formula detection model** released!
+
+- [2024-03-25] TexTeller2.0 released! The training data for TexTeller2.0 has been increased to 7.5M (15x more than TexTeller1.0 and also improved in data quality). The trained TexTeller2.0 demonstrated **superior performance** in the test set, especially in recognizing rare symbols, complex multi-line formulas, and matrices.
+
+  > [Here](./assets/test.pdf) are more test images and a horizontal comparison of various recognition models.
+
+## 🚀 Getting Started
+
+1. Install uv:
+
+   ```bash
+   pip install uv
+   ```
+
+2. Install the project's dependencies:
+
+   ```bash
+   uv pip install texteller
+   ```
+
+3. If your are using CUDA backend, you may need to install `onnxruntime-gpu`:
+
+   ```bash
+   uv pip install texteller[onnxruntime-gpu]
+   ```
+
+4. Run the following command to start inference:
+
+   ```bash
+   texteller inference "/path/to/image.{jpg,png}"
+   ```
+
+   > See `texteller inference --help` for more details
+
+## 🌐 Web Demo
+
+Run the following command:
+
+```bash
+texteller web
+```
+
+Enter `http://localhost:8501` in a browser to view the web demo.
+
+> [!NOTE]
+> Paragraph recognition cannot restore the structure of a document, it can only recognize its content.
+
+## 🖥️ Server
+
+We use [ray serve](https://github.com/ray-project/ray) to provide an API server for TexTeller. To start the server, run the following command:
+
+```bash
+texteller launch
+```
+
+| Parameter | Description |
+| --------- | -------- |
+| `-ckpt` | The path to the weights file,*default is TexTeller's pretrained weights*. |
+| `-tknz` | The path to the tokenizer,*default is TexTeller's tokenizer*. |
+| `-p` | The server's service port,*default is 8000*. |
+| `--num-replicas` | The number of service replicas to run on the server,*default is 1 replica*. You can use more replicas to achieve greater throughput.|
+| `--ncpu-per-replica` | The number of CPU cores used per service replica,*default is 1*.|
+| `--ngpu-per-replica` | The number of GPUs used per service replica,*default is 1*. You can set this value between 0 and 1 to run multiple service replicas on one GPU to share the GPU, thereby improving GPU utilization. (Note, if --num_replicas is 2, --ngpu_per_replica is 0.7, then 2 GPUs must be available) |
+| `--num-beams` | The number of beams for beam search,*default is 1*. |
+| `--use-onnx` | Perform inference using Onnx Runtime, *disabled by default* |
+
+To send requests to the server:
+
+```python
+# client_demo.py
+
+import requests
+
+server_url = "http://127.0.0.1:8000/predict"
+
+img_path = "/path/to/your/image"
+with open(img_path, 'rb') as img:
+    files = {'img': img}
+    response = requests.post(server_url, files=files)
+
+print(response.text)
+```
+
+## 🐍 Python API
+
+We provide several easy-to-use Python APIs for formula OCR scenarios. Please refer to our [documentation](https://oleehyo.github.io/TexTeller/) to learn about the corresponding API interfaces and usage.
+
+## 🔍 Formula Detection
+
+TexTeller's formula detection model is trained on 3,415 images of Chinese materials and 8,272 images from the [IBEM dataset](https://zenodo.org/records/4757865).
+
+<div align="center">
+    <img src="./assets/det_rec.png" width=250>
+</div>
+
+We provide a formula detection interface in the Python API. Please refer to our [API documentation](https://oleehyo.github.io/TexTeller/) for more details.
+
+## 🏋️‍♂️ Training
+
+Please setup your environment before training:
+
+1. Install the dependencies for training:
+
+   ```bash
+   uv pip install texteller[train]
+   ```
+
+2. Clone the repository:
+
+   ```bash
+   git clone https://github.com/OleehyO/TexTeller.git
+   ```
+
+### Dataset
+
+We provide an example dataset in the `examples/train_texteller/dataset/train` directory, you can place your own training data according to the format of the example dataset.
+
+### Training the Model
+
+In the `examples/train_texteller/` directory, run the following command:
+
+   ```bash
+   accelerate launch train.py
+   ```
+
+Training arguments can be adjusted in [`train_config.yaml`](./examples/train_texteller/train_config.yaml).
+
+## 📅 Plans
+
+- [X] ~~Train the model with a larger dataset~~
+- [X] ~~Recognition of scanned images~~
+- [X] ~~Support for English and Chinese scenarios~~
+- [X] ~~Handwritten formulas support~~
+- [ ] PDF document recognition
+- [ ] Inference acceleration
+
+## ⭐️ Stargazers over time
+
+[![Stargazers over time](https://starchart.cc/OleehyO/TexTeller.svg?variant=adaptive)](https://starchart.cc/OleehyO/TexTeller)
+
+## 👥 Contributors
+
+<a href="https://github.com/OleehyO/TexTeller/graphs/contributors">
+   <a href="https://github.com/OleehyO/TexTeller/graphs/contributors">
+      <img src="https://contrib.rocks/image?repo=OleehyO/TexTeller" />
+   </a>
+</a>
--- a/assets/README_zh.md
+++ b/assets/README_zh.md
@@ -0,0 +1,215 @@
+📄 中文 | [English](./README.md)
+
+<div align="center">
+    <h1>
+        <img src="./fire.svg" width=30, height=30>
+        𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛
+        <img src="./fire.svg" width=30, height=30>
+    </h1>
+
+  [![](https://img.shields.io/badge/API-文档-orange.svg?logo=read-the-docs)](https://oleehyo.github.io/TexTeller/)
+  [![](https://img.shields.io/badge/docker-镜像-green.svg?logo=docker)](https://hub.docker.com/r/oleehyo/texteller)
+  [![](https://img.shields.io/badge/数据-Texteller1.0-brightgreen.svg?logo=huggingface)](https://huggingface.co/datasets/OleehyO/latex-formulas)
+  [![](https://img.shields.io/badge/权重-Texteller3.0-yellow.svg?logo=huggingface)](https://huggingface.co/OleehyO/TexTeller)
+  [![](https://img.shields.io/badge/协议-Apache_2.0-blue.svg?logo=github)](https://opensource.org/licenses/Apache-2.0)
+
+</div>
+
+https://github.com/OleehyO/TexTeller/assets/56267907/532d1471-a72e-4960-9677-ec6c19db289f
+
+TexTeller 是一个端到端的公式识别模型，能够将图像转换为对应的 LaTeX 公式。
+
+TexTeller 使用 **8千万图像-公式对** 进行训练（前代数据集可在此[获取](https://huggingface.co/datasets/OleehyO/latex-formulas)），相较 [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) 使用的 10 万量级数据集，TexTeller 具有**更强的泛化能力**和**更高的准确率**，覆盖绝大多数使用场景。
+
+>[!NOTE]
+> 如果您想对本项目提出反馈或建议，欢迎前往 [讨论区](https://github.com/OleehyO/TexTeller/discussions) 发起讨论。
+
+---
+
+<table>
+<tr>
+<td>
+
+## 🔖 目录
+- [快速开始](#-快速开始)
+- [网页演示](#-网页演示)
+- [服务部署](#-服务部署)
+- [Python接口](#-python接口)
+- [公式检测](#-公式检测)
+- [模型训练](#️️-模型训练)
+
+</td>
+<td>
+
+<div align="center">
+  <figure>
+    <img src="cover.png" width="800">
+    <figcaption>
+      <p>TexTeller 可识别的图像示例</p>
+    </figcaption>
+  </figure>
+  <div>
+  </div>
+</div>
+
+</td>
+</tr>
+</table>
+
+## 📮 更新日志
+
+- [2024-06-06] **TexTeller3.0 发布！** 训练数据增至 **8千万**（是 TexTeller2.0 的 **10倍** 并提升了数据多样性）。TexTeller3.0 新特性：
+
+  - 支持扫描件、手写公式、中英文混合公式识别
+
+  - 支持印刷体中英文混排公式的OCR识别
+
+- [2024-05-02] 支持**段落识别**功能
+
+- [2024-04-12] **公式检测模型**发布！
+
+- [2024-03-25] TexTeller2.0 发布！TexTeller2.0 的训练数据增至750万（是前代的15倍并提升了数据质量）。训练后的 TexTeller2.0 在测试集中展现了**更优性能**，特别是在识别罕见符号、复杂多行公式和矩阵方面表现突出。
+
+  > [此处](./assets/test.pdf) 展示了更多测试图像及各类识别模型的横向对比。
+
+## 🚀 快速开始
+
+1. 安装uv：
+
+   ```bash
+   pip install uv
+   ```
+
+2. 安装项目依赖：
+
+   ```bash
+   uv pip install texteller
+   ```
+
+3. 若使用 CUDA 后端，可能需要安装 `onnxruntime-gpu`：
+
+   ```bash
+   uv pip install texteller[onnxruntime-gpu]
+   ```
+
+4. 运行以下命令开始推理：
+
+   ```bash
+   texteller inference "/path/to/image.{jpg,png}"
+   ```
+
+   > 更多参数请查看 `texteller inference --help`
+
+## 🌐 网页演示
+
+命令行运行：
+
+```bash
+texteller web
+```
+
+在浏览器中输入 `http://localhost:8501` 查看网页演示。
+
+> [!NOTE]
+> 段落识别无法还原文档结构，仅能识别其内容。
+
+## 🖥️ 服务部署
+
+我们使用 [ray serve](https://github.com/ray-project/ray) 为 TexTeller 提供 API 服务。启动服务：
+
+```bash
+texteller launch
+```
+
+| 参数 | 说明 |
+| --------- | -------- |
+| `-ckpt` | 权重文件路径，*默认为 TexTeller 预训练权重* |
+| `-tknz` | 分词器路径，*默认为 TexTeller 分词器* |
+| `-p` | 服务端口，*默认 8000* |
+| `--num-replicas` | 服务副本数，*默认 1*。可使用更多副本来提升吞吐量 |
+| `--ncpu-per-replica` | 单个副本使用的CPU核数，*默认 1* |
+| `--ngpu-per-replica` | 单个副本使用的GPU数，*默认 1*。可设置为0~1之间的值来在单卡上运行多个服务副本共享GPU，提升GPU利用率（注意，若--num_replicas为2，--ngpu_per_replica为0.7，则需有2块可用GPU） |
+| `--num-beams` | beam search的束宽，*默认 1* |
+| `--use-onnx` | 使用Onnx Runtime进行推理，*默认关闭* |
+
+向服务发送请求：
+
+```python
+# client_demo.py
+
+import requests
+
+server_url = "http://127.0.0.1:8000/predict"
+
+img_path = "/path/to/your/image"
+with open(img_path, 'rb') as img:
+    files = {'img': img}
+    response = requests.post(server_url, files=files)
+
+print(response.text)
+```
+
+## 🐍 Python接口
+
+我们为公式OCR场景提供了多个易用的Python API接口，请参考[接口文档](https://oleehyo.github.io/TexTeller/)了解对应的API接口及使用方法。
+
+## 🔍 公式检测
+
+TexTeller的公式检测模型在3415张中文资料图像和8272张[IBEM数据集](https://zenodo.org/records/4757865)图像上训练。
+
+<div align="center">
+    <img src="./det_rec.png" width=250>
+</div>
+
+我们在Python接口中提供了公式检测接口，详见[接口文档](https://oleehyo.github.io/TexTeller/)。
+
+## 🏋️‍♂️ 模型训练
+
+请按以下步骤配置训练环境：
+
+1. 安装训练依赖：
+
+   ```bash
+   uv pip install texteller[train]
+   ```
+
+2. 克隆仓库：
+
+   ```bash
+   git clone https://github.com/OleehyO/TexTeller.git
+   ```
+
+### 数据集准备
+
+我们在`examples/train_texteller/dataset/train`目录中提供了示例数据集，您可按照示例数据集的格式放置自己的训练数据。
+
+### 开始训练
+
+在`examples/train_texteller/`目录下运行：
+
+   ```bash
+   accelerate launch train.py
+   ```
+
+训练参数可通过[`train_config.yaml`](./examples/train_texteller/train_config.yaml)调整。
+
+## 📅 计划列表
+
+- [X] ~~使用更大规模数据集训练模型~~
+- [X] ~~扫描件识别支持~~
+- [X] ~~中英文场景支持~~
+- [X] ~~手写公式支持~~
+- [ ] PDF文档识别
+- [ ] 推理加速
+
+## ⭐️ 项目星标
+
+[![Star增长曲线](https://starchart.cc/OleehyO/TexTeller.svg?variant=adaptive)](https://starchart.cc/OleehyO/TexTeller)
+
+## 👥 贡献者
+
+<a href="https://github.com/OleehyO/TexTeller/graphs/contributors">
+   <a href="https://github.com/OleehyO/TexTeller/graphs/contributors">
+      <img src="https://contrib.rocks/image?repo=OleehyO/TexTeller" />
+   </a>
+</a>
--- a/assets/cover.png
+++ b/assets/cover.png
--- a/assets/det_rec.png
+++ b/assets/det_rec.png
--- a/assets/fire.svg
+++ b/assets/fire.svg
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+    <feGaussianBlur in="SourceGraphic" stdDeviation="3"></feGaussianBlur>
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+</circle><circle cx="20" cy="114.80243604193255" r="9" fill="#f8b26a">
+  <animate attributeName="cy" values="114.80243604193255;7.19374553530416" keyTimes="0;1" dur="1s" repeatCount="indefinite" begin="-0.6866227460985781s"></animate>
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+</circle></g>
+</svg>
--- a/assets/logo.svg
+++ b/assets/logo.svg
@@ -0,0 +1,14 @@
+
+<svg xmlns="http://www.w3.org/2000/svg" width="430" height="80" viewBox="0 0 430 80">
+
+  <text
+    x="50%"
+    y="50%"
+    font-family="monaco"
+    font-size="55"
+    text-anchor="middle"
+    dominant-baseline="middle">
+      <tspan fill="#F37726">{</tspan><tspan fill="#616161">Tex</tspan><tspan fill="#F37726">}</tspan><tspan fill="#616161">Teller</tspan>
+  </text>
+
+</svg>
--- a/assets/scss.png
+++ b/assets/scss.png
--- a/assets/test.pdf
+++ b/assets/test.pdf
--- a/assets/web_demo.gif
+++ b/assets/web_demo.gif
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
--- a/docs/make.bat
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -0,0 +1,39 @@
+API Reference
+=============
+
+This section provides detailed API documentation for the TexTeller package. TexTeller is a tool for detecting and recognizing LaTeX formulas in images and converting mixed text and formula images to markdown.
+
+.. contents:: Table of Contents
+   :local:
+   :depth: 2
+
+
+Image to LaTeX Conversion
+-------------------------
+
+.. autofunction:: texteller.api.img2latex
+
+Paragraph to Markdown Conversion
+------------------------------
+
+.. autofunction:: texteller.api.paragraph2md
+
+LaTeX Detection
+---------------
+
+.. autofunction:: texteller.api.detection.latex_detect
+
+Model Loading
+-------------
+
+.. autofunction:: texteller.api.load_model
+.. autofunction:: texteller.api.load_tokenizer
+.. autofunction:: texteller.api.load_latexdet_model
+.. autofunction:: texteller.api.load_textdet_model
+.. autofunction:: texteller.api.load_textrec_model
+
+
+KaTeX Conversion
+----------------
+
+.. autofunction:: texteller.api.to_katex
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -0,0 +1,75 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute.
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+# -- Project information -----------------------------------------------------
+
+project = "TexTeller"
+copyright = "2025, TexTeller Team"
+author = "TexTeller Team"
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = [
+    "myst_parser",
+    "sphinx.ext.duration",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.autosectionlabel",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.autosummary",
+    "sphinx_copybutton",
+    # 'sphinx.ext.linkcode',
+    # 'sphinxarg.ext',
+    "sphinx_design",
+    "nbsphinx",
+]
+
+templates_path = ["_templates"]
+exclude_patterns = []
+
+# Autodoc settings
+autodoc_member_order = "bysource"
+add_module_names = False
+autoclass_content = "both"
+autodoc_default_options = {
+    "members": True,
+    "member-order": "bysource",
+    "undoc-members": True,
+    "show-inheritance": True,
+    "imported-members": True,
+}
+
+# Intersphinx settings
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3", None),
+    "numpy": ("https://numpy.org/doc/stable", None),
+    "torch": ("https://pytorch.org/docs/stable", None),
+    "transformers": ("https://huggingface.co/docs/transformers/main/en", None),
+}
+
+html_theme = "sphinx_book_theme"
+
+html_theme_options = {
+    "repository_url": "https://github.com/OleehyO/TexTeller",
+    "use_repository_button": True,
+    "use_issues_button": True,
+    "use_edit_page_button": True,
+    "use_download_button": True,
+}
+
+html_logo = "../../assets/logo.svg"
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -0,0 +1,76 @@
+.. TexTeller documentation master file, created by
+   sphinx-quickstart on Sun Apr 20 13:05:53 2025.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+TexTeller Documentation
+===========================================
+
+Features
+--------
+
+- **Image to LaTeX Conversion**: Convert images containing LaTeX formulas to LaTeX code
+- **LaTeX Detection**: Detect and locate LaTeX formulas in mixed text/formula images
+- **Paragraph to Markdown**: Convert mixed text and formula images to Markdown format
+
+Installation
+-----------
+
+You can install TexTeller using pip:
+
+.. code-block:: bash
+
+   pip install texteller
+
+Quick Start
+----------
+
+Converting an image to LaTeX:
+
+.. code-block:: python
+
+   from texteller import load_model, load_tokenizer, img2latex
+
+   # Load models
+   model = load_model(use_onnx=False)
+   tokenizer = load_tokenizer()
+
+   # Convert image to LaTeX
+   latex = img2latex(model, tokenizer, ["path/to/image.png"])[0]
+
+Processing a mixed text/formula image:
+
+.. code-block:: python
+
+   from texteller import (
+       load_model, load_tokenizer, load_latexdet_model,
+       load_textdet_model, load_textrec_model, paragraph2md
+   )
+
+   # Load all required models
+   latex_model = load_model()
+   tokenizer = load_tokenizer()
+   latex_detector = load_latexdet_model()
+   text_detector = load_textdet_model()
+   text_recognizer = load_textrec_model()
+
+   # Convert to markdown
+   markdown = paragraph2md(
+       "path/to/mixed_image.png",
+       latex_detector,
+       text_detector,
+       text_recognizer,
+       latex_model,
+       tokenizer
+   )
+
+API Documentation
+----------------
+
+For detailed API documentation, please see :doc:`./api`.
+
+.. toctree::
+   :maxdepth: 2
+   :hidden:
+
+   api
--- a/examples/client_demo.py
+++ b/examples/client_demo.py
@@ -0,0 +1,10 @@
+import requests
+
+server_url = "http://127.0.0.1:8000/predict"
+
+img_path = "/path/to/your/image"
+with open(img_path, "rb") as img:
+    files = {"img": img}
+    response = requests.post(server_url, files=files)
+
+print(response.text)
--- a/examples/train_texteller/dataset/train/0.png
+++ b/examples/train_texteller/dataset/train/0.png
--- a/examples/train_texteller/dataset/train/1.png
+++ b/examples/train_texteller/dataset/train/1.png
--- a/examples/train_texteller/dataset/train/10.png
+++ b/examples/train_texteller/dataset/train/10.png
--- a/examples/train_texteller/dataset/train/11.png
+++ b/examples/train_texteller/dataset/train/11.png
--- a/examples/train_texteller/dataset/train/12.png
+++ b/examples/train_texteller/dataset/train/12.png
--- a/examples/train_texteller/dataset/train/13.png
+++ b/examples/train_texteller/dataset/train/13.png
--- a/examples/train_texteller/dataset/train/14.png
+++ b/examples/train_texteller/dataset/train/14.png
--- a/examples/train_texteller/dataset/train/15.png
+++ b/examples/train_texteller/dataset/train/15.png
--- a/examples/train_texteller/dataset/train/16.png
+++ b/examples/train_texteller/dataset/train/16.png
--- a/examples/train_texteller/dataset/train/17.png
+++ b/examples/train_texteller/dataset/train/17.png
--- a/examples/train_texteller/dataset/train/18.png
+++ b/examples/train_texteller/dataset/train/18.png
--- a/examples/train_texteller/dataset/train/19.png
+++ b/examples/train_texteller/dataset/train/19.png
--- a/examples/train_texteller/dataset/train/2.png
+++ b/examples/train_texteller/dataset/train/2.png
--- a/examples/train_texteller/dataset/train/20.png
+++ b/examples/train_texteller/dataset/train/20.png
--- a/examples/train_texteller/dataset/train/21.png
+++ b/examples/train_texteller/dataset/train/21.png
--- a/examples/train_texteller/dataset/train/22.png
+++ b/examples/train_texteller/dataset/train/22.png
--- a/examples/train_texteller/dataset/train/23.png
+++ b/examples/train_texteller/dataset/train/23.png
--- a/examples/train_texteller/dataset/train/24.png
+++ b/examples/train_texteller/dataset/train/24.png
--- a/examples/train_texteller/dataset/train/25.png
+++ b/examples/train_texteller/dataset/train/25.png
--- a/examples/train_texteller/dataset/train/26.png
+++ b/examples/train_texteller/dataset/train/26.png
--- a/examples/train_texteller/dataset/train/27.png
+++ b/examples/train_texteller/dataset/train/27.png
--- a/examples/train_texteller/dataset/train/28.png
+++ b/examples/train_texteller/dataset/train/28.png
--- a/examples/train_texteller/dataset/train/29.png
+++ b/examples/train_texteller/dataset/train/29.png
--- a/examples/train_texteller/dataset/train/3.png
+++ b/examples/train_texteller/dataset/train/3.png
--- a/examples/train_texteller/dataset/train/30.png
+++ b/examples/train_texteller/dataset/train/30.png
--- a/examples/train_texteller/dataset/train/31.png
+++ b/examples/train_texteller/dataset/train/31.png
--- a/examples/train_texteller/dataset/train/32.png
+++ b/examples/train_texteller/dataset/train/32.png
--- a/examples/train_texteller/dataset/train/33.png
+++ b/examples/train_texteller/dataset/train/33.png
--- a/examples/train_texteller/dataset/train/34.png
+++ b/examples/train_texteller/dataset/train/34.png
--- a/examples/train_texteller/dataset/train/4.png
+++ b/examples/train_texteller/dataset/train/4.png
--- a/examples/train_texteller/dataset/train/5.png
+++ b/examples/train_texteller/dataset/train/5.png
--- a/examples/train_texteller/dataset/train/6.png
+++ b/examples/train_texteller/dataset/train/6.png
--- a/examples/train_texteller/dataset/train/7.png
+++ b/examples/train_texteller/dataset/train/7.png
--- a/examples/train_texteller/dataset/train/8.png
+++ b/examples/train_texteller/dataset/train/8.png
--- a/examples/train_texteller/dataset/train/9.png
+++ b/examples/train_texteller/dataset/train/9.png
--- a/examples/train_texteller/dataset/train/metadata.jsonl
+++ b/examples/train_texteller/dataset/train/metadata.jsonl
@@ -0,0 +1,35 @@
+{"file_name": "0.png", "latex_formula": "\\[\\mathbb{C}^{4}\\stackrel{{\\pi_{1}}}{{\\longleftarrow}}\\mathcal{ F}\\stackrel{{\\pi_{2}}}{{\\rightarrow}}\\mathcal{PT},\\]"}
+{"file_name": "1.png", "latex_formula": "\\[W^{*}_{Z}(x_{1},x_{2})=W_{f\\lrcorner Z}(y_{1},y_{2})=\\mathcal{P}\\exp\\left( \\int_{\\gamma}A_{\\mu}dx^{\\mu}\\right).\\]"}
+{"file_name": "2.png", "latex_formula": "\\[G=W^{*}_{Z}(q,p)=\\tilde{H}H^{-1}\\]"}
+{"file_name": "3.png", "latex_formula": "\\[H=W^{*}_{Z}(p,x),\\ \\ \\tilde{H}=W^{*}_{Z}(q,x).\\]"}
+{"file_name": "4.png", "latex_formula": "\\[v\\cdot f^{*}A|_{x}=(f\\lrcorner Z)_{*}v\\cdot A|_{f\\lrcorner Z(x)},\\quad x\\in Z, \\ v\\in T_{x}Z.\\]"}
+{"file_name": "5.png", "latex_formula": "\\[(f\\lrcorner Z)_{*}v\\cdot A|_{f\\lrcorner Z(x)}=v^{\\alpha\\dot{\\alpha}}\\Big{(} \\frac{\\partial y^{\\beta\\dot{\\beta}}}{\\partial x^{\\alpha\\dot{\\alpha}}}A_{\\beta \\dot{\\beta}}\\Big{)}\\Big{|}_{f\\lrcorner Z(x)},\\ x\\in Z,\\ v\\in T_{x}Z,\\]"}
+{"file_name": "6.png", "latex_formula": "\\[\\{T_{i},T_{j}\\}=\\{\\tilde{T}^{i},\\tilde{T}^{j}\\}=0,\\ \\ \\{T_{i},\\tilde{T}^{j}\\}=2i \\delta^{j}_{i}D,\\]"}
+{"file_name": "7.png", "latex_formula": "\\[(\\partial_{s},q_{i},\\tilde{q}^{k})\\rightarrow(D,M^{j}_{i}T_{j},\\tilde{M}^{k}_ {l}\\tilde{T}^{l}),\\]"}
+{"file_name": "8.png", "latex_formula": "\\[M^{i}_{j}\\tilde{M}^{j}_{k}=\\delta^{i}_{k}.\\]"}
+{"file_name": "9.png", "latex_formula": "\\[Q_{i\\alpha}=q_{i\\alpha}+\\omega_{i\\alpha},\\ \\tilde{Q}^{i}_{\\dot{\\alpha}}=q^{i}_{ \\dot{\\alpha}}+\\tilde{\\omega}^{i}_{\\dot{\\alpha}},\\ D_{\\alpha\\dot{\\alpha}}= \\partial_{\\alpha\\dot{\\alpha}}+A_{\\alpha\\dot{\\alpha}}.\\]"}
+{"file_name": "10.png", "latex_formula": "\\[\\hat{f}(g,\\theta^{i\\alpha},\\tilde{\\theta}^{\\dot{\\alpha}}_{j})=(f(g),[V^{-1}]^ {\\alpha}_{\\beta}\\theta^{i\\beta},[\\tilde{V}^{-1}]^{\\dot{\\alpha}}_{\\dot{\\beta}} \\tilde{\\theta}^{\\dot{\\beta}}_{j}),\\ g\\in{\\cal G},\\]"}
+{"file_name": "11.png", "latex_formula": "\\[v^{\\beta\\dot{\\beta}}V^{\\alpha}_{\\beta}\\tilde{V}^{\\dot{\\alpha}}_{\\dot{\\beta}} =((f\\lrcorner L_{0})_{*}v)^{\\alpha\\dot{\\alpha}},\\]"}
+{"file_name": "12.png", "latex_formula": "\\[\\omega_{i\\alpha}=\\tilde{\\theta}^{\\dot{\\alpha}}_{i}h_{\\alpha\\dot{\\alpha}}(x^{ \\beta\\dot{\\beta}},\\tau^{\\beta\\dot{\\beta}}),\\ \\ \\tilde{\\omega}^{i}_{\\alpha}=\\theta^{i\\alpha}\\tilde{h}_{\\alpha\\dot{\\alpha}}(x^{ \\beta\\dot{\\beta}},\\tau^{\\beta\\dot{\\beta}}),\\]"}
+{"file_name": "13.png", "latex_formula": "\\[\\begin{split}&\\lambda^{\\alpha}\\hat{f}^{*}\\omega_{i\\alpha}(z)= \\tilde{\\theta}^{\\dot{\\beta}}_{i}\\lambda^{\\alpha}\\left(V^{\\beta}_{\\alpha}h_{ \\beta\\dot{\\beta}}(x^{\\prime},\\tau^{\\prime})\\right),\\\\ &\\tilde{\\lambda}^{\\dot{\\alpha}}\\hat{f}^{*}\\tilde{\\omega}^{i}_{ \\dot{\\alpha}}(z)=\\theta^{i\\beta}\\tilde{\\lambda}^{\\dot{\\alpha}}\\left(\\tilde{V}^ {\\dot{\\beta}}_{\\dot{\\alpha}}\\tilde{h}_{\\beta\\dot{\\beta}}(x^{\\prime},\\tau^{ \\prime})\\right),\\end{split}\\]"}
+{"file_name": "14.png", "latex_formula": "\\[A_{\\alpha\\dot{\\alpha}}=A_{\\alpha\\dot{\\alpha}}(x^{\\beta\\dot{\\beta}},\\tau^{ \\beta\\dot{\\beta}})\\]"}
+{"file_name": "15.png", "latex_formula": "\\[D=\\lambda^{\\alpha}\\tilde{\\lambda}^{\\dot{\\alpha}}D_{\\alpha\\dot{\\alpha}}\\]"}
+{"file_name": "16.png", "latex_formula": "\\[D=\\lambda^{\\alpha}\\tilde{\\lambda}^{\\dot{\\alpha}}\\partial_{\\alpha\\dot{\\alpha}}\\]"}
+{"file_name": "17.png", "latex_formula": "\\[[v_{1}\\cdot D^{*},v_{2}\\cdot D^{*}]=0\\]"}
+{"file_name": "18.png", "latex_formula": "\\[\\Phi_{A}=(\\omega_{i\\alpha},\\tilde{\\omega}^{i}_{\\dot{\\alpha}},A_{\\alpha\\dot{ \\alpha}})\\]"}
+{"file_name": "19.png", "latex_formula": "\\[\\hat{f}:{\\cal F}^{6|4N}\\rightarrow{\\cal F}^{6|4N}\\]"}
+{"file_name": "20.png", "latex_formula": "\\[\\sigma=(s,\\xi^{i},\\tilde{\\xi}_{j})\\in\\mathbb{C}^{1|2N}\\]"}
+{"file_name": "21.png", "latex_formula": "\\[\\tau^{\\alpha\\dot{\\alpha}}(h_{\\alpha\\dot{\\alpha}}+\\tilde{h}_{\\alpha\\dot{\\alpha} })=0\\]"}
+{"file_name": "22.png", "latex_formula": "\\[\\tau^{\\alpha\\dot{\\alpha}}\\rightarrow[V^{-1}]^{\\alpha}_{\\beta}[\\tilde{V}^{-1}]^{ \\dot{\\alpha}}_{\\dot{\\beta}}\\tau^{\\beta\\dot{\\beta}}\\]"}
+{"file_name": "23.png", "latex_formula": "\\[\\tau^{\\beta\\dot{\\beta}}=\\sum_{i}\\theta^{i\\beta}\\tilde{\\theta}^{\\dot{\\beta}}_{i}\\]"}
+{"file_name": "24.png", "latex_formula": "\\[\\theta^{i\\alpha}\\omega_{i\\alpha}+\\tilde{\\theta}^{i}_{\\dot{\\alpha}}\\tilde{ \\omega}^{\\dot{\\alpha}}_{i}=0\\]"}
+{"file_name": "25.png", "latex_formula": "\\[\\tilde{T}^{i}=\\tilde{\\lambda}^{\\dot{\\alpha}}\\tilde{Q}^{i}_{\\dot{\\alpha}}\\]"}
+{"file_name": "26.png", "latex_formula": "\\[\\tilde{T}^{i}=\\tilde{\\lambda}^{\\dot{\\alpha}}\\tilde{q}^{i}_{\\dot{\\alpha}}\\]"}
+{"file_name": "27.png", "latex_formula": "\\[\\tilde{\\lambda}^{\\dot{\\alpha}}f^{*}A_{\\alpha\\dot{\\alpha}}=H^{-1}\\tilde{ \\lambda}^{\\dot{\\alpha}}\\partial_{\\alpha\\dot{\\alpha}}H\\]"}
+{"file_name": "28.png", "latex_formula": "\\[\\tilde{q}^{i}=\\partial_{\\tilde{\\xi}_{i}}+i\\xi^{i}\\partial_{s}\\]"}
+{"file_name": "29.png", "latex_formula": "\\[\\tilde{q}^{i}_{\\dot{\\alpha}}=\\frac{\\partial}{\\partial\\tilde{\\theta}^{\\dot{ \\alpha}}_{i}}+i\\theta^{i\\alpha}\\frac{\\partial}{\\partial x^{\\alpha\\dot{\\alpha}}}\\]"}
+{"file_name": "30.png", "latex_formula": "\\[f\\lrcorner L(z)=\\pi_{1}\\circ f(z,\\lambda,\\tilde{\\lambda})\\ \\forall z\\in L\\]"}
+{"file_name": "31.png", "latex_formula": "\\[q_{i\\alpha}=\\frac{\\partial}{\\partial\\theta^{i\\alpha}}+i\\tilde{\\theta}^{\\dot{ \\alpha}}_{i}\\frac{\\partial}{\\partial x^{\\alpha\\dot{\\alpha}}}\\]"}
+{"file_name": "32.png", "latex_formula": "\\[q_{i}=\\partial_{\\xi^{i}}+i\\tilde{\\xi}_{i}\\partial_{s}\\]"}
+{"file_name": "33.png", "latex_formula": "\\[v^{\\alpha\\dot{\\alpha}}=\\lambda^{\\alpha}\\tilde{\\lambda}^{\\dot{\\alpha}}\\]"}
+{"file_name": "34.png", "latex_formula": "\\[z^{A}=(x^{\\alpha\\dot{\\alpha}},\\theta^{i\\alpha},\\tilde{\\theta}^{\\dot{\\alpha}}_{ j})\\]"}
--- a/examples/train_texteller/train.py
+++ b/examples/train_texteller/train.py
@@ -0,0 +1,71 @@
+from functools import partial
+
+import yaml
+from datasets import load_dataset
+from transformers import (
+    Trainer,
+    TrainingArguments,
+)
+
+from texteller import load_model, load_tokenizer
+from texteller.constants import MIN_HEIGHT, MIN_WIDTH
+
+from examples.train_texteller.utils import (
+    collate_fn,
+    filter_fn,
+    img_inf_transform,
+    img_train_transform,
+    tokenize_fn,
+)
+
+
+def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer):
+    training_args = TrainingArguments(**training_config)
+    trainer = Trainer(
+        model,
+        training_args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        tokenizer=tokenizer,
+        data_collator=collate_fn_with_tokenizer,
+    )
+
+    trainer.train(resume_from_checkpoint=None)
+
+
+if __name__ == "__main__":
+    dataset = load_dataset("imagefolder", data_dir="dataset")["train"]
+    dataset = dataset.filter(
+        lambda x: x["image"].height > MIN_HEIGHT and x["image"].width > MIN_WIDTH
+    )
+    dataset = dataset.shuffle(seed=42)
+    dataset = dataset.flatten_indices()
+
+    tokenizer = load_tokenizer()
+    # If you want use your own tokenizer, please modify the path to your tokenizer
+    # tokenizer = load_tokenizer("/path/to/your/tokenizer")
+    filter_fn_with_tokenizer = partial(filter_fn, tokenizer=tokenizer)
+    dataset = dataset.filter(filter_fn_with_tokenizer, num_proc=8)
+
+    map_fn = partial(tokenize_fn, tokenizer=tokenizer)
+    tokenized_dataset = dataset.map(
+        map_fn, batched=True, remove_columns=dataset.column_names, num_proc=8
+    )
+
+    # Split dataset into train and eval, ratio 9:1
+    split_dataset = tokenized_dataset.train_test_split(test_size=0.1, seed=42)
+    train_dataset, eval_dataset = split_dataset["train"], split_dataset["test"]
+    train_dataset = train_dataset.with_transform(img_train_transform)
+    eval_dataset = eval_dataset.with_transform(img_inf_transform)
+    collate_fn_with_tokenizer = partial(collate_fn, tokenizer=tokenizer)
+
+    # Train from scratch
+    model = load_model()
+
+    # If you want to train from pre-trained model, please modify the path to your pre-trained checkpoint
+    # model = load_model("/path/to/your/model_checkpoint")
+
+    enable_train = True
+    training_config = yaml.safe_load(open("train_config.yaml"))
+    if enable_train:
+        train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer)
--- a/examples/train_texteller/train_config.yaml
+++ b/examples/train_texteller/train_config.yaml
@@ -0,0 +1,32 @@
+# For more information, please refer to the official documentation: https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.TrainingArguments
+
+seed: 42  # Random seed for reproducibility
+use_cpu: false  # Whether to use CPU (it's easier to debug with CPU when starting to test the code)
+learning_rate: 5.0e-5  # Learning rate
+num_train_epochs: 10  # Total number of training epochs
+per_device_train_batch_size: 4  # Batch size per GPU for training
+per_device_eval_batch_size: 8  # Batch size per GPU for evaluation
+output_dir: "train_result"  # Output directory
+overwrite_output_dir: false  # If the output directory exists, do not delete its content
+report_to:
+  - tensorboard  # Report logs to TensorBoard
+save_strategy: "steps"  # Strategy to save checkpoints
+save_steps: 500  # Interval of steps to save checkpoints, can be int or a float (0~1), when float it represents the ratio of total training steps (e.g., can set to 1.0 / 2000)
+save_total_limit: 5  # Maximum number of models to save. The oldest models will be deleted if this number is exceeded
+logging_strategy: "steps"  # Log every certain number of steps
+logging_steps: 500  # Number of steps between each log
+logging_nan_inf_filter: false  # Record logs for loss=nan or inf
+optim: "adamw_torch"  # Optimizer
+lr_scheduler_type: "cosine"  # Learning rate scheduler
+warmup_ratio: 0.1  # Ratio of warmup steps in total training steps (e.g., for 1000 steps, the first 100 steps gradually increase lr from 0 to the set lr)
+max_grad_norm: 1.0  # For gradient clipping, ensure the norm of the gradients does not exceed 1.0 (default 1.0)
+fp16: false  # Whether to use 16-bit floating point for training (generally not recommended, as loss can easily explode)
+bf16: false  # Whether to use Brain Floating Point (bfloat16) for training (recommended if architecture supports it)
+gradient_accumulation_steps: 1  # Gradient accumulation steps, consider this parameter to achieve large batch size effects when batch size cannot be large
+jit_mode_eval: false  # Whether to use PyTorch jit trace during eval (can speed up the model, but the model must be static, otherwise will throw errors)
+torch_compile: false  # Whether to use torch.compile to compile the model (for better training and inference performance)
+dataloader_pin_memory: true  # Can speed up data transfer between CPU and GPU
+dataloader_num_workers: 1  # Default is not to use multiprocessing for data loading, usually set to 4*number of GPUs used
+evaluation_strategy: "steps"  # Evaluation strategy, can be "steps" or "epoch"
+eval_steps: 500  # If evaluation_strategy="step"
+remove_unused_columns: false  # Don't change this unless you really know what you are doing.
--- a/examples/train_texteller/utils/init.py
+++ b/examples/train_texteller/utils/init.py
@@ -0,0 +1,17 @@
+from .functional import (
+    collate_fn,
+    filter_fn,
+    tokenize_fn,
+)
+from .transforms import (
+    img_train_transform,
+    img_inf_transform,
+)
+
+__all__ = [
+    "collate_fn",
+    "filter_fn",
+    "tokenize_fn",
+    "img_train_transform",
+    "img_inf_transform",
+]
--- a/examples/train_texteller/utils/augraphy_pipe.py
+++ b/examples/train_texteller/utils/augraphy_pipe.py
@@ -1,14 +1,40 @@
-from augraphy import *
+"""
+Custom augraphy pipeline for training
+
+This file implements a custom augraphy data augmentation pipeline. We found that using augraphy's
+default pipeline can cause significant degradation to formula images, potentially losing semantic
+information. Therefore, we carefully selected several common augmentation effects,
+adjusting their parameters and combination methods to preserve the original semantic information
+of the images as much as possible.
+"""
+
+from augraphy import (
+    InkColorSwap,
+    LinesDegradation,
+    OneOf,
+    Dithering,
+    InkBleed,
+    InkShifter,
+    NoiseTexturize,
+    BrightnessTexturize,
+    ColorShift,
+    DirtyDrum,
+    LightingGradient,
+    Brightness,
+    Gamma,
+    SubtleNoise,
+    Jpeg,
+    AugraphyPipeline,
+)
 import random

-def ocr_augmentation_pipeline():
-    pre_phase = [
-        # Rescale(scale="optimal", target_dpi = 300,  p = 1.0),
-    ]
+
+def get_custom_augraphy():
+    pre_phase = []

    ink_phase = [
        InkColorSwap(
-            ink_swap_color="lhy_custom",
+            ink_swap_color="random",
            ink_swap_sequence_number_range=(5, 10),
            ink_swap_min_width_range=(2, 3),
            ink_swap_max_width_range=(100, 120),
@@ -16,7 +42,7 @@ def ocr_augmentation_pipeline():
            ink_swap_max_height_range=(100, 120),
            ink_swap_min_area_range=(10, 20),
            ink_swap_max_area_range=(400, 500),
-            p=0.2
+            p=0.2,
        ),
        LinesDegradation(
            line_roi=(0.0, 0.0, 1.0, 1.0),
@@ -28,9 +54,8 @@ def ocr_augmentation_pipeline():
            line_long_to_short_ratio=(5, 7),
            line_replacement_probability=(0.4, 0.5),
            line_replacement_thickness=(1, 3),
-            p=0.2
+            p=0.2,
        ),
-
        #  ============================
        OneOf(
            [
@@ -44,10 +69,9 @@ def ocr_augmentation_pipeline():
                    severity=(0.4, 0.6),
                ),
            ],
-            p=0.2
+            p=0.2,
        ),
        #  ============================
-
        #  ============================
        InkShifter(
            text_shift_scale_range=(18, 27),
@@ -56,38 +80,32 @@ def ocr_augmentation_pipeline():
            blur_kernel_size=(5, 5),
            blur_sigma=0,
            noise_type="perlin",
-            p=0.2
+            p=0.2,
        ),
        #  ============================
-
    ]

    paper_phase = [
-        NoiseTexturize(  # tested
+        NoiseTexturize(
            sigma_range=(3, 10),
            turbulence_range=(2, 5),
            texture_width_range=(300, 500),
            texture_height_range=(300, 500),
-            p=0.2
+            p=0.2,
        ),
-        BrightnessTexturize(  # tested
-            texturize_range=(0.9, 0.99),
-            deviation=0.03,
-            p=0.2
-        )
+        BrightnessTexturize(texturize_range=(0.9, 0.99), deviation=0.03, p=0.2),
    ]

    post_phase = [
-        ColorShift(  # tested
+        ColorShift(
            color_shift_offset_x_range=(3, 5),
            color_shift_offset_y_range=(3, 5),
            color_shift_iterations=(2, 3),
            color_shift_brightness_range=(0.9, 1.1),
            color_shift_gaussian_kernel_range=(3, 3),
-            p=0.2
+            p=0.2,
        ),
-
-        DirtyDrum(  # tested
+        DirtyDrum(
            line_width_range=(1, 6),
            line_concentration=random.uniform(0.05, 0.15),
            direction=random.randint(0, 2),
@@ -95,9 +113,8 @@ def ocr_augmentation_pipeline():
            noise_value=(64, 224),
            ksize=random.choice([(3, 3), (5, 5), (7, 7)]),
            sigmaX=0,
-            p=0.2
+            p=0.2,
        ),
-
        # =====================================
        OneOf(
            [
@@ -119,10 +136,9 @@ def ocr_augmentation_pipeline():
                    gamma_range=(0.9, 1.1),
                ),
            ],
-            p=0.2
+            p=0.2,
        ),
        # =====================================
-
        # =====================================
        OneOf(
            [
@@ -130,10 +146,10 @@ def ocr_augmentation_pipeline():
                    subtle_range=random.randint(5, 10),
                ),
                Jpeg(
-                    quality_range=(85, 95),
+                    quality_range=(70, 95),
                ),
            ],
-            p=0.2
+            p=0.2,
        ),
        # =====================================
    ]
@@ -143,7 +159,7 @@ def ocr_augmentation_pipeline():
        paper_phase=paper_phase,
        post_phase=post_phase,
        pre_phase=pre_phase,
-        log=False
+        log=False,
    )

    return pipeline
--- a/examples/train_texteller/utils/functional.py
+++ b/examples/train_texteller/utils/functional.py
@@ -0,0 +1,47 @@
+from typing import Any
+
+import torch
+from transformers import DataCollatorForLanguageModeling
+
+from texteller.constants import MAX_TOKEN_SIZE, MIN_HEIGHT, MIN_WIDTH
+
+
+def _left_move(x: torch.Tensor, pad_val):
+    assert len(x.shape) == 2, "x should be 2-dimensional"
+    lefted_x = torch.ones_like(x)
+    lefted_x[:, :-1] = x[:, 1:]
+    lefted_x[:, -1] = pad_val
+    return lefted_x
+
+
+def tokenize_fn(samples: dict[str, list[Any]], tokenizer=None) -> dict[str, list[Any]]:
+    assert tokenizer is not None, "tokenizer should not be None"
+    tokenized_formula = tokenizer(samples["latex_formula"], return_special_tokens_mask=True)
+    tokenized_formula["pixel_values"] = samples["image"]
+    return tokenized_formula
+
+
+def collate_fn(samples: list[dict[str, Any]], tokenizer=None) -> dict[str, list[Any]]:
+    assert tokenizer is not None, "tokenizer should not be None"
+    pixel_values = [dic.pop("pixel_values") for dic in samples]
+
+    clm_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+    batch = clm_collator(samples)
+    batch["pixel_values"] = pixel_values
+    batch["decoder_input_ids"] = batch.pop("input_ids")
+    batch["decoder_attention_mask"] = batch.pop("attention_mask")
+
+    batch["labels"] = _left_move(batch["labels"], -100)
+
+    # convert list of Image to a tensor with (B, C, H, W)
+    batch["pixel_values"] = torch.stack(batch["pixel_values"], dim=0)
+    return batch
+
+
+def filter_fn(sample, tokenizer=None) -> bool:
+    return (
+        sample["image"].height > MIN_HEIGHT
+        and sample["image"].width > MIN_WIDTH
+        and len(tokenizer(sample["latex_formula"])["input_ids"]) < MAX_TOKEN_SIZE - 10
+    )
--- a/examples/train_texteller/utils/transforms.py
+++ b/examples/train_texteller/utils/transforms.py
@@ -0,0 +1,154 @@
+import torch
+import random
+import numpy as np
+import cv2
+
+from torchvision.transforms import v2
+from typing import Any
+from PIL import Image
+from collections import Counter
+
+from texteller.constants import (
+    IMG_CHANNELS,
+    MAX_RESIZE_RATIO,
+    MIN_RESIZE_RATIO,
+)
+from texteller.utils import transform as inference_transform
+from .augraphy_pipe import get_custom_augraphy
+
+augraphy_pipeline = get_custom_augraphy()
+
+
+def trim_white_border(image: np.ndarray):
+    if len(image.shape) != 3 or image.shape[2] != 3:
+        raise ValueError("Image is not in RGB format or channel is not in third dimension")
+
+    if image.dtype != np.uint8:
+        raise ValueError(f"Image should stored in uint8")
+
+    corners = [tuple(image[0, 0]), tuple(image[0, -1]), tuple(image[-1, 0]), tuple(image[-1, -1])]
+    bg_color = Counter(corners).most_common(1)[0][0]
+    bg_color_np = np.array(bg_color, dtype=np.uint8)
+
+    h, w = image.shape[:2]
+    bg = np.full((h, w, 3), bg_color_np, dtype=np.uint8)
+
+    diff = cv2.absdiff(image, bg)
+    mask = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
+
+    threshold = 15
+    _, diff = cv2.threshold(mask, threshold, 255, cv2.THRESH_BINARY)
+
+    x, y, w, h = cv2.boundingRect(diff)
+
+    trimmed_image = image[y : y + h, x : x + w]
+
+    return trimmed_image
+
+
+def add_white_border(image: np.ndarray, max_size: int) -> np.ndarray:
+    randi = [random.randint(0, max_size) for _ in range(4)]
+    pad_height_size = randi[1] + randi[3]
+    pad_width_size = randi[0] + randi[2]
+    if pad_height_size + image.shape[0] < 30:
+        compensate_height = int((30 - (pad_height_size + image.shape[0])) * 0.5) + 1
+        randi[1] += compensate_height
+        randi[3] += compensate_height
+    if pad_width_size + image.shape[1] < 30:
+        compensate_width = int((30 - (pad_width_size + image.shape[1])) * 0.5) + 1
+        randi[0] += compensate_width
+        randi[2] += compensate_width
+    return v2.functional.pad(
+        torch.from_numpy(image).permute(2, 0, 1),
+        padding=randi,
+        padding_mode="constant",
+        fill=(255, 255, 255),
+    )
+
+
+def padding(images: list[torch.Tensor], required_size: int) -> list[torch.Tensor]:
+    images = [
+        v2.functional.pad(
+            img, padding=[0, 0, required_size - img.shape[2], required_size - img.shape[1]]
+        )
+        for img in images
+    ]
+    return images
+
+
+def random_resize(images: list[np.ndarray], minr: float, maxr: float) -> list[np.ndarray]:
+    if len(images[0].shape) != 3 or images[0].shape[2] != 3:
+        raise ValueError("Image is not in RGB format or channel is not in third dimension")
+
+    ratios = [random.uniform(minr, maxr) for _ in range(len(images))]
+    return [
+        # Anti-aliasing
+        cv2.resize(
+            img, (int(img.shape[1] * r), int(img.shape[0] * r)), interpolation=cv2.INTER_LANCZOS4
+        )
+        for img, r in zip(images, ratios)
+    ]
+
+
+def rotate(image: np.ndarray, min_angle: int, max_angle: int) -> np.ndarray:
+    # Get the center of the image to define the point of rotation
+    image_center = tuple(np.array(image.shape[1::-1]) / 2)
+
+    # Generate a random angle within the specified range
+    angle = random.randint(min_angle, max_angle)
+
+    # Get the rotation matrix for rotating the image around its center
+    rotation_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
+
+    # Determine the size of the rotated image
+    cos = np.abs(rotation_mat[0, 0])
+    sin = np.abs(rotation_mat[0, 1])
+    new_width = int((image.shape[0] * sin) + (image.shape[1] * cos))
+    new_height = int((image.shape[0] * cos) + (image.shape[1] * sin))
+
+    # Adjust the rotation matrix to take into account translation
+    rotation_mat[0, 2] += (new_width / 2) - image_center[0]
+    rotation_mat[1, 2] += (new_height / 2) - image_center[1]
+
+    # Rotate the image with the specified border color (white in this case)
+    rotated_image = cv2.warpAffine(
+        image, rotation_mat, (new_width, new_height), borderValue=(255, 255, 255)
+    )
+
+    return rotated_image
+
+
+def ocr_aug(image: np.ndarray) -> np.ndarray:
+    if random.random() < 0.2:
+        image = rotate(image, -5, 5)
+    image = add_white_border(image, max_size=25).permute(1, 2, 0).numpy()
+    image = augraphy_pipeline(image)
+    return image
+
+
+def train_transform(images: list[Image.Image]) -> list[torch.Tensor]:
+    assert IMG_CHANNELS == 1, "Only support grayscale images for now"
+
+    images = [np.array(img.convert("RGB")) for img in images]
+    # random resize first
+    images = random_resize(images, MIN_RESIZE_RATIO, MAX_RESIZE_RATIO)
+    images = [trim_white_border(image) for image in images]
+
+    # OCR augmentation
+    images = [ocr_aug(image) for image in images]
+
+    # general transform pipeline
+    images = inference_transform(images)
+    return images
+
+
+def img_train_transform(samples: dict[str, list[Any]]) -> dict[str, list[Any]]:
+    processed_img = train_transform(samples["pixel_values"])
+    samples["pixel_values"] = processed_img
+    return samples
+
+
+def img_inf_transform(samples: dict[str, list[Any]]) -> dict[str, list[Any]]:
+    processed_img = inference_transform(samples["pixel_values"])
+    samples["pixel_values"] = processed_img
+    return samples
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,87 @@
+[build-system]
+requires = ["hatchling", "hatch-vcs"]
+build-backend = "hatchling.build"
+
+[project]
+name = "texteller"
+authors = [
+    { name="OleehyO", email="leehy0357@gmail.com" }
+]
+dynamic = ["version"]
+description = "Texteller is a tool for converting rendered image to original latex code"
+readme = "README.md"
+license = { file = "LICENSE" }
+requires-python = ">=3.10"
+dependencies = [
+    "click>=8.1.8",
+    "colorama>=0.4.6",
+    "opencv-python-headless>=4.11.0.86",
+    "pyclipper>=1.3.0.post6",
+    "shapely>=2.1.0",
+    "streamlit>=1.44.1",
+    "streamlit-paste-button>=0.1.2",
+    "torch>=2.6.0",
+    "torchvision>=0.21.0",
+    "transformers==4.47",
+    "wget>=3.2",
+    "optimum[onnxruntime]>=1.24.0",
+    "python-multipart>=0.0.20",
+    "ray[serve]>=2.44.1",
+]
+
+[tool.hatch.version]
+source = "vcs"
+
+[tool.ruff]
+exclude = [".git", ".mypy_cache", ".ruff_cache", ".venv", "dist"]
+target-version = "py310"
+line-length = 100
+
+[tool.ruff.format]
+line-ending = "lf"
+quote-style = "double"
+
+[tool.ruff.lint]
+select = ["E", "W"]
+ignore = [
+    "E999",
+    "EXE001",
+    "UP009",
+    "F401",
+    "TID252",
+    "F403",
+    "F841",
+    "E501",
+    "W291",
+    "W293",
+    "E741",
+    "E712",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["texteller"]
+
+[project.scripts]
+texteller = "texteller.cli:cli"
+
+[project.optional-dependencies]
+onnxruntime-gpu = [
+    "onnxruntime-gpu>=1.21.0",
+]
+test = [
+    "pytest>=8.3.5",
+]
+train = [
+    "accelerate>=1.6.0",
+    "augraphy>=8.2.6",
+    "datasets>=3.5.0",
+    "tensorboardx>=2.6.2.2",
+]
+docs = [
+    "myst-parser>=4.0.1",
+    "nbsphinx>=0.9.7",
+    "sphinx>=8.1.3",
+    "sphinx-book-theme>=1.1.4",
+    "sphinx-copybutton>=0.5.2",
+    "sphinx-design>=0.6.1",
+]
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,13 +0,0 @@
-transformers
-datasets
-evaluate
-streamlit
-opencv-python
-ray[serve]
-accelerate
-tensorboardX
-nltk
-python-multipart
-
-pdf2image
-augraphy
--- a/src/client_demo.py
+++ b/src/client_demo.py
@@ -1,16 +0,0 @@
-import requests
-
-# 服务的 URL
-url = "http://127.0.0.1:9900/predict"
-
-# 替换成你要预测的图像的路径
-img_path = "/home/lhy/code/TeXify/src/7.png"
-
-# 构造请求数据
-data = {"img_path": img_path}
-
-# 发送 POST 请求
-response = requests.post(url, json=data)
-
-# 打印响应
-print(response.text)
--- a/src/inference.py
+++ b/src/inference.py
@@ -1,40 +0,0 @@
-import os
-import argparse
-
-from pathlib import Path
-from models.ocr_model.utils.inference import inference
-from models.ocr_model.model.TexTeller import TexTeller
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '-img', 
-        type=str, 
-        required=True,
-        help='path to the input image'
-    )
-    parser.add_argument(
-        '-cuda', 
-        default=False,
-        action='store_true',
-        help='use cuda or not'
-    )
-
-    args = parser.parse_args([
-        '-img', './models/ocr_model/test_img/1.png',
-        '-cuda'
-    ])
-
-    script_dirpath = Path(__file__).resolve().parent
-    os.chdir(script_dirpath)
-
-    model = TexTeller.from_pretrained('./models/ocr_model/model_checkpoint')
-    tokenizer = TexTeller.get_tokenizer('./models/tokenizer/roberta-tokenizer-550K')
-
-    # base = '/home/lhy/code/TeXify/src/models/ocr_model/test_img'
-    # img_path = [base + f'/{i}.png' for i in range(7, 12)]
-    img_path = [args.img]
-
-    res = inference(model, tokenizer, img_path, args.cuda)
-    print(res[0])
--- a/src/models/globals.py
+++ b/src/models/globals.py
@@ -1,60 +0,0 @@
-# 公式图片(灰度化后)的均值和方差
-IMAGE_MEAN = 0.9545467
-IMAGE_STD  = 0.15394445
-
-
-# =========================   ocr模型用的参数   ============================= #
-
-# 输入图片的最大最小的宽和高
-MIN_HEIGHT = 32
-MAX_HEIGHT = 512
-MIN_WIDTH  = 32
-MAX_WIDTH  = 1280
-# LaTex-OCR中分别是 32、192、32、672
-
-# ocr模型所用数据集，pdf转图片所用的Density值(dpi)
-TEXIFY_INPUT_DENSITY = 100
-
-# ocr模型的tokenizer中的词典数量
-VOCAB_SIZE = 15000
-
-# ocr模型是否固定输入图片的大小
-OCR_FIX_SIZE = True
-# ocr模型训练时，输入图片所固定的大小 (when OCR_FIX_SIZE is True)
-OCR_IMG_SIZE = 448
-# ocr模型训练时，输入图片最大的宽和高（when OCR_FIX_SIZE is False）
-OCR_IMG_MAX_HEIGHT = 512
-OCR_IMG_MAX_WIDTH  = 768
-
-# ocr模型输入图片的通道数
-OCR_IMG_CHANNELS = 1  # 灰度图
-
-# ocr模型训练数据集的最长token数
-MAX_TOKEN_SIZE = 1024     # 模型最长的embedding长度(默认512)
-# MAX_TOKEN_SIZE = 2048     # 模型最长的embedding长度(默认512)
-# MAX_TOKEN_SIZE = 600
-
-# ocr模型训练时随机缩放的比例
-MAX_RESIZE_RATIO = 1.15
-MIN_RESIZE_RATIO = 0.75
-
-# ocr模型输入的图片要求的最低宽和高(过滤垃圾数据)
-MIN_HEIGHT = 12
-MIN_WIDTH  = 30
-
-# ============================================================================= #
-
-
-# =========================   Resizer模型用的参数   ============================= #
-
-# Resizer模型所用数据集中，图片所用的Density渲染值
-RESIZER_INPUT_DENSITY  = 200   
-
-LABEL_RATIO   = 1.0 * TEXIFY_INPUT_DENSITY / RESIZER_INPUT_DENSITY
-
-NUM_CLASSES   = 1      # 模型使用回归预测
-NUM_CHANNELS  = 1      # 输入单通道图片（灰度图）
-
-# Resizer在训练时，图片所固定的的大小
-RESIZER_IMG_SIZE = 448    
-# ============================================================================= #
--- a/src/models/ocr_model/README.md
+++ b/src/models/ocr_model/README.md
@@ -1,6 +0,0 @@
-* Encoder-Decoder架构
-
-* Encoder使用Deit_{BASE}
-
-* Decoder使用RoBERTa_{LARGE}
-    * Decoder的tokenizer也使用RoBERTa_{LARGE}的
--- a/src/models/ocr_model/model/TexTeller.py
+++ b/src/models/ocr_model/model/TexTeller.py
@@ -1,65 +0,0 @@
-from pathlib import Path
-
-from models.globals import (
-    VOCAB_SIZE,
-    OCR_IMG_SIZE,
-    OCR_IMG_CHANNELS,
-    MAX_TOKEN_SIZE
-)
-
-from transformers import (
-    ViTConfig,
-    ViTModel,
-    TrOCRConfig,
-    TrOCRForCausalLM,
-    RobertaTokenizerFast,
-    VisionEncoderDecoderModel,
-)
-
-
-class TexTeller(VisionEncoderDecoderModel):
-    def __init__(self, decoder_path=None, tokenizer_path=None):
-        encoder = ViTModel(ViTConfig(
-            image_size=OCR_IMG_SIZE,
-            num_channels=OCR_IMG_CHANNELS
-        ))
-        decoder = TrOCRForCausalLM(TrOCRConfig(
-            vocab_size=VOCAB_SIZE,
-            max_position_embeddings=MAX_TOKEN_SIZE
-        ))
-        super().__init__(encoder=encoder, decoder=decoder)
-    
-    @classmethod
-    def from_pretrained(cls, model_path: str):
-        model_path = Path(model_path).resolve()
-        return VisionEncoderDecoderModel.from_pretrained(str(model_path))
-
-    @classmethod
-    def get_tokenizer(cls, tokenizer_path: str) -> RobertaTokenizerFast:
-        tokenizer_path = Path(tokenizer_path).resolve()
-        return RobertaTokenizerFast.from_pretrained(str(tokenizer_path))
-
-
-if __name__ == "__main__":
-    pause = 1
-    # texteller = TexTeller()
-    # from ..utils.inference import inference
-    # model = TexTeller.from_pretrained('/home/lhy/code/TexTeller/src/models/ocr_model/model/ckpt')
-    # model.save_pretrained('/home/lhy/code/TexTeller/src/models/ocr_model/model/ckpt2', safe_serialization=False)
-    # tokenizer = TexTeller.get_tokenizer('/home/lhy/code/TeXify/src/models/tokenizer/roberta-tokenizer-550Kformulas')
-
-    # base = '/home/lhy/code/TeXify/src/models/ocr_model/model'
-    # imgs_path = [
-    #     # base + '/1.jpg',
-    #     # base + '/2.jpg',
-    #     # base + '/3.jpg',
-    #     # base + '/4.jpg',
-    #     # base + '/5.jpg',
-    #     # base + '/6.jpg',
-    #     base + '/foo.jpg'
-    # ]
-
-    # # res = inference(model, [img1, img2, img3, img4, img5, img6, img7], tokenizer)
-    # res = inference(model, imgs_path, tokenizer)
-    # pause = 1
-
--- a/src/models/ocr_model/train/fonts/JINKY.ttf
+++ b/src/models/ocr_model/train/fonts/JINKY.ttf
--- a/src/models/ocr_model/train/fonts/Rotodesign
+++ b/src/models/ocr_model/train/fonts/Rotodesign
@@ -1,14 +0,0 @@
-Congratulations on your download of this fine Rotodesign brand font product. We hope it will bring you many hours of typesetting pleasure and riches beyond your wildest dreams. We DO NOT, however, guarantee either of these things. Your mileage may vary. 
-
-This font is freeware, and is provided with no warranties as to its quality or its utility. After all, how much did you pay? Anyway, this font can be copied and used as you wish provided all copies include this readme file. Don't lie to your friends and tell 'em you made it yourself. You only cheat yourself when you do that. In the unlikely event you use this font to design something really cool or that makes you a ton of cash money, that's okay with me, just send me a copy or two of the finished item, and remember me when you get rich and famous. Enjoy!
-
-©2006 
-Patrick Broderick
-Rotodesign
-
-http://www.rotodesign.com
-roto@rotodesign.net
-
-Rotodesign
-1288 Columbus Ave. #176
-San Francisco, CA 94133
--- a/src/models/ocr_model/train/fonts/font_type.zip
+++ b/src/models/ocr_model/train/fonts/font_type.zip
--- a/src/models/ocr_model/train/google_bleu/google_bleu.py
+++ b/src/models/ocr_model/train/google_bleu/google_bleu.py
@@ -1,168 +0,0 @@
-# Copyright 2020 The HuggingFace Evaluate Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Google BLEU (aka GLEU) metric. """
-
-from typing import Dict, List
-
-import datasets
-from nltk.translate import gleu_score
-
-import evaluate
-from evaluate import MetricInfo
-
-from .tokenizer_13a import Tokenizer13a
-
-
-_CITATION = """\
-@misc{wu2016googles,
-      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
-      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
-              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
-              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
-              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
-              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
-              and Jeffrey Dean},
-      year={2016},
-      eprint={1609.08144},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-"""
-
-_DESCRIPTION = """\
-The BLEU score has some undesirable properties when used for single
-sentences, as it was designed to be a corpus measure. We therefore
-use a slightly different score for our RL experiments which we call
-the 'GLEU score'. For the GLEU score, we record all sub-sequences of
-1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
-compute a recall, which is the ratio of the number of matching n-grams
-to the number of total n-grams in the target (ground truth) sequence,
-and a precision, which is the ratio of the number of matching n-grams
-to the number of total n-grams in the generated output sequence. Then
-GLEU score is simply the minimum of recall and precision. This GLEU
-score's range is always between 0 (no matches) and 1 (all match) and
-it is symmetrical when switching output and target. According to
-our experiments, GLEU score correlates quite well with the BLEU
-metric on a corpus level but does not have its drawbacks for our per
-sentence reward objective.
-"""
-
-_KWARGS_DESCRIPTION = """\
-Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
-Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
-tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
-
-Args:
-    predictions (list of str): list of translations to score.
-    references (list of list of str): list of lists of references for each translation.
-    tokenizer : approach used for tokenizing `predictions` and `references`.
-        The default tokenizer is `tokenizer_13a`, a minimal tokenization approach that is equivalent to `mteval-v13a`, used by WMT.
-        This can be replaced by any function that takes a string as input and returns a list of tokens as output.
-    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
-    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
-
-Returns:
-    'google_bleu': google_bleu score
-
-Examples:
-    Example 1:
-        >>> predictions = ['It is a guide to action which ensures that the rubber duck always disobeys the commands of the cat', \
-        'he read the book because he was interested in world history']
-        >>> references = [['It is the guiding principle which guarantees the rubber duck forces never being under the command of the cat'], \
-        ['he was interested in world history because he read the book']]
-        >>> google_bleu = evaluate.load("google_bleu")
-        >>> results = google_bleu.compute(predictions=predictions, references=references)
-        >>> print(round(results["google_bleu"], 2))
-        0.44
-
-    Example 2:
-        >>> predictions = ['It is a guide to action which ensures that the rubber duck always disobeys the commands of the cat', \
-        'he read the book because he was interested in world history']
-        >>> references = [['It is the guiding principle which guarantees the rubber duck forces never being under the command of the cat', \
-        'It is a guide to action that ensures that the rubber duck will never heed the cat commands', \
-        'It is the practical guide for the rubber duck army never to heed the directions of the cat'], \
-        ['he was interested in world history because he read the book']]
-        >>> google_bleu = evaluate.load("google_bleu")
-        >>> results = google_bleu.compute(predictions=predictions, references=references)
-        >>> print(round(results["google_bleu"], 2))
-        0.61
-
-    Example 3:
-        >>> predictions = ['It is a guide to action which ensures that the rubber duck always disobeys the commands of the cat', \
-        'he read the book because he was interested in world history']
-        >>> references = [['It is the guiding principle which guarantees the rubber duck forces never being under the command of the cat', \
-        'It is a guide to action that ensures that the rubber duck will never heed the cat commands', \
-        'It is the practical guide for the rubber duck army never to heed the directions of the cat'], \
-        ['he was interested in world history because he read the book']]
-        >>> google_bleu = evaluate.load("google_bleu")
-        >>> results = google_bleu.compute(predictions=predictions, references=references, min_len=2)
-        >>> print(round(results["google_bleu"], 2))
-        0.53
-
-    Example 4:
-        >>> predictions = ['It is a guide to action which ensures that the rubber duck always disobeys the commands of the cat', \
-        'he read the book because he was interested in world history']
-        >>> references = [['It is the guiding principle which guarantees the rubber duck forces never being under the command of the cat', \
-        'It is a guide to action that ensures that the rubber duck will never heed the cat commands', \
-        'It is the practical guide for the rubber duck army never to heed the directions of the cat'], \
-        ['he was interested in world history because he read the book']]
-        >>> google_bleu = evaluate.load("google_bleu")
-        >>> results = google_bleu.compute(predictions=predictions,references=references, min_len=2, max_len=6)
-        >>> print(round(results["google_bleu"], 2))
-        0.4
-"""
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class GoogleBleu(evaluate.Metric):
-    def _info(self) -> MetricInfo:
-        return evaluate.MetricInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            features=[
-                datasets.Features(
-                    {
-                        "predictions": datasets.Value("string", id="sequence"),
-                        "references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"),
-                    }
-                ),
-                datasets.Features(
-                    {
-                        "predictions": datasets.Value("string", id="sequence"),
-                        "references": datasets.Value("string", id="sequence"),
-                    }
-                ),
-            ],
-        )
-
-    def _compute(
-        self,
-        predictions: List[str],
-        references: List[List[str]],
-        tokenizer=Tokenizer13a(),
-        min_len: int = 1,
-        max_len: int = 4,
-    ) -> Dict[str, float]:
-        # if only one reference is provided make sure we still use list of lists
-        if isinstance(references[0], str):
-            references = [[ref] for ref in references]
-
-        references = [[tokenizer(r) for r in ref] for ref in references]
-        predictions = [tokenizer(p) for p in predictions]
-        return {
-            "google_bleu": gleu_score.corpus_gleu(
-                list_of_references=references, hypotheses=predictions, min_len=min_len, max_len=max_len
-            )
-        }
--- a/src/models/ocr_model/train/google_bleu/tokenizer_13a.py
+++ b/src/models/ocr_model/train/google_bleu/tokenizer_13a.py
@@ -1,100 +0,0 @@
-# Source: https://github.com/mjpost/sacrebleu/blob/master/sacrebleu/tokenizers/tokenizer_13a.py
-# Copyright 2020 SacreBLEU Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import re
-from functools import lru_cache
-
-
-class BaseTokenizer:
-    """A base dummy tokenizer to derive from."""
-
-    def signature(self):
-        """
-        Returns a signature for the tokenizer.
-        :return: signature string
-        """
-        return "none"
-
-    def __call__(self, line):
-        """
-        Tokenizes an input line with the tokenizer.
-        :param line: a segment to tokenize
-        :return: the tokenized line
-        """
-        return line
-
-
-class TokenizerRegexp(BaseTokenizer):
-    def signature(self):
-        return "re"
-
-    def __init__(self):
-        self._re = [
-            # language-dependent part (assuming Western languages)
-            (re.compile(r"([\{-\~\[-\` -\&\(-\+\:-\@\/])"), r" \1 "),
-            # tokenize period and comma unless preceded by a digit
-            (re.compile(r"([^0-9])([\.,])"), r"\1 \2 "),
-            # tokenize period and comma unless followed by a digit
-            (re.compile(r"([\.,])([^0-9])"), r" \1 \2"),
-            # tokenize dash when preceded by a digit
-            (re.compile(r"([0-9])(-)"), r"\1 \2 "),
-            # one space only between words
-            # NOTE: Doing this in Python (below) is faster
-            # (re.compile(r'\s+'), r' '),
-        ]
-
-    @lru_cache(maxsize=2**16)
-    def __call__(self, line):
-        """Common post-processing tokenizer for `13a` and `zh` tokenizers.
-        :param line: a segment to tokenize
-        :return: the tokenized line
-        """
-        for (_re, repl) in self._re:
-            line = _re.sub(repl, line)
-
-        # no leading or trailing spaces, single space within words
-        # return ' '.join(line.split())
-        # This line is changed with regards to the original tokenizer (seen above) to return individual words
-        return line.split()
-
-
-class Tokenizer13a(BaseTokenizer):
-    def signature(self):
-        return "13a"
-
-    def __init__(self):
-        self._post_tokenizer = TokenizerRegexp()
-
-    @lru_cache(maxsize=2**16)
-    def __call__(self, line):
-        """Tokenizes an input line using a relatively minimal tokenization
-        that is however equivalent to mteval-v13a, used by WMT.
-
-        :param line: a segment to tokenize
-        :return: the tokenized line
-        """
-
-        # language-independent part:
-        line = line.replace("<skipped>", "")
-        line = line.replace("-\n", "")
-        line = line.replace("\n", " ")
-
-        if "&" in line:
-            line = line.replace("&quot;", '"')
-            line = line.replace("&amp;", "&")
-            line = line.replace("&lt;", "<")
-            line = line.replace("&gt;", ">")
-
-        return self._post_tokenizer(f" {line} ")
--- a/src/models/ocr_model/train/train.py
+++ b/src/models/ocr_model/train/train.py
@@ -1,114 +0,0 @@
-import os
-
-from functools import partial
-from pathlib import Path
-
-from datasets import load_dataset
-from transformers import Trainer, TrainingArguments, Seq2SeqTrainer, Seq2SeqTrainingArguments, GenerationConfig
-
-from .training_args import CONFIG
-from ..model.TexTeller import TexTeller
-from ..utils.functional import tokenize_fn, collate_fn, img_train_transform, img_inf_transform, filter_fn
-from ..utils.metrics import bleu_metric
-from ...globals import MAX_TOKEN_SIZE, MIN_WIDTH, MIN_HEIGHT   
-
-
-def train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer):
-    training_args = TrainingArguments(**CONFIG)
-    debug_mode = False
-    if debug_mode:
-        training_args.auto_find_batch_size = False
-        training_args.num_train_epochs = 2
-        # training_args.per_device_train_batch_size = 3
-        training_args.per_device_train_batch_size = 2
-        training_args.per_device_eval_batch_size = 2 * training_args.per_device_train_batch_size
-        training_args.jit_mode_eval = False
-        training_args.torch_compile = False
-        training_args.dataloader_num_workers = 1
-    
-    trainer = Trainer(
-        model,
-        training_args,
-
-        train_dataset=train_dataset,
-        eval_dataset=eval_dataset,
-
-        tokenizer=tokenizer, 
-        data_collator=collate_fn_with_tokenizer,
-    )
-
-    trainer.train(resume_from_checkpoint=None)
-    # trainer.train(resume_from_checkpoint='/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv2/checkpoint-288000')
-
-
-def evaluate(model, tokenizer, eval_dataset, collate_fn):
-    eval_config = CONFIG.copy()
-    eval_config['predict_with_generate'] = True
-    generate_config = GenerationConfig(
-        max_length=MAX_TOKEN_SIZE-100,
-        num_beams=1,
-        do_sample=False,
-        pad_token_id=tokenizer.pad_token_id,
-        eos_token_id=tokenizer.eos_token_id,
-        bos_token_id=tokenizer.bos_token_id,
-    )
-    eval_config['generation_config'] = generate_config
-    eval_config['auto_find_batch_size'] = False
-    seq2seq_config = Seq2SeqTrainingArguments(**eval_config)
-
-    trainer = Seq2SeqTrainer(
-        model,
-        seq2seq_config,
-
-        eval_dataset=eval_dataset,
-        tokenizer=tokenizer, 
-        data_collator=collate_fn,
-        compute_metrics=partial(bleu_metric, tokenizer=tokenizer)
-    )
-
-    res = trainer.evaluate()
-    print(res)
-    
-
-if __name__ == '__main__':
-    cur_path = os.getcwd()
-    script_dirpath = Path(__file__).resolve().parent
-    os.chdir(script_dirpath)
-
-    dataset = load_dataset(
-        '/home/lhy/code/TexTeller/src/models/ocr_model/train/data/loader.py'
-    )['train']
-    tokenizer = TexTeller.get_tokenizer('/home/lhy/code/TexTeller/src/models/tokenizer/roberta-tokenizer-7Mformulas')
-    filter_fn_with_tokenizer = partial(filter_fn, tokenizer=tokenizer)
-
-    # dataset = dataset.filter(lambda x: x['image'].height > MIN_HEIGHT and x['image'].width > MIN_WIDTH)
-    dataset = dataset.filter(filter_fn_with_tokenizer, num_proc=16)
-    dataset = dataset.shuffle(seed=42)
-    dataset = dataset.flatten_indices()
-
-    map_fn = partial(tokenize_fn, tokenizer=tokenizer)
-    tokenized_dataset = dataset.map(map_fn, batched=True, remove_columns=dataset.column_names, num_proc=8, load_from_cache_file=True)
-
-    split_dataset = tokenized_dataset.train_test_split(test_size=0.005, seed=42)
-    train_dataset, eval_dataset = split_dataset['train'], split_dataset['test']
-
-    train_dataset = train_dataset.with_transform(img_train_transform)
-    eval_dataset  = eval_dataset.with_transform(img_inf_transform)
-
-    collate_fn_with_tokenizer = partial(collate_fn, tokenizer=tokenizer)
-    # model = TexTeller()
-    model = TexTeller.from_pretrained('/home/lhy/code/TexTeller/src/models/ocr_model/model/ckpt')
-
-    # =================  debug  =======================
-    # foo = train_dataset[:50]
-    # bar = eval_dataset[:50]
-    # =================  debug  =======================
-
-    enable_train    = True
-    enable_evaluate = True
-    if enable_train:
-        train(model, tokenizer, train_dataset, eval_dataset, collate_fn_with_tokenizer)
-    if enable_evaluate:
-        evaluate(model, tokenizer, eval_dataset, collate_fn_with_tokenizer)
-
-    os.chdir(cur_path)
--- a/src/models/ocr_model/train/training_args.py
+++ b/src/models/ocr_model/train/training_args.py
@@ -1,84 +0,0 @@
-CONFIG = {
-    "seed": 42,                            # 随机种子，用于确保实验的可重复性
-    "use_cpu": False,                      # 是否使用cpu（刚开始测试代码的时候先用cpu跑会更容易debug）
-    # "data_seed": 42,                     # data sampler的采样也固定
-    # "full_determinism": True,            # 使整个训练完全固定（这个设置会有害于模型训练，只用于debug）
-
-    "output_dir": "train_result/TexTellerv3",          # 输出目录
-    "overwrite_output_dir": False,         # 如果输出目录存在，不删除原先的内容
-    "report_to": ["tensorboard"],          # 输出日志到TensorBoard，
-                                           #+通过在命令行：tensorboard --logdir ./logs 来查看日志
-
-    "logging_dir": None,                   # TensorBoard日志文件的存储目录(使用默认值)
-    "log_level": "warning",                   # 其他可选:‘debug’, ‘info’, ‘warning’, ‘error’ and ‘critical’（由低级别到高级别）
-    "logging_strategy": "steps",           # 每隔一定步数记录一次日志
-    "logging_steps": 4000,                  # 记录日志的步数间隔，可以是int也可以是(0~1)的float，当是float时表示总的训练步数的ratio(比方说可以设置成1.0 / 2000)
-                                           #+通常与eval_steps一致
-    "logging_nan_inf_filter": False,       # 对loss=nan或inf进行记录
-
-    "num_train_epochs": 4,                # 总的训练轮数
-    # "max_steps": 3,                      # 训练的最大步骤数。如果设置了这个参数，
-                                           #+那么num_train_epochs将被忽略（通常用于调试）
-
-    # "label_names": ['your_label_name'],  # 指定data_loader中的标签名，如果不指定则默认为'labels'
-
-    "per_device_train_batch_size": 3,    # 每个GPU的batch size
-    "per_device_eval_batch_size": 6,      # 每个GPU的evaluation batch size
-    # "auto_find_batch_size": True,          # 自动搜索合适的batch size（指数decay）
-    "auto_find_batch_size": False,          # 自动搜索合适的batch size（指数decay）
-
-    "optim": "adamw_torch",                # 还提供了很多AdamW的变体（相较于经典的AdamW更加高效）
-                                           #+当设置了optim后，就不需要在Trainer中传入optimizer
-    "lr_scheduler_type": "cosine",         # 设置lr_scheduler
-    "warmup_ratio": 0.1,                   # warmup占整个训练steps的比例(假如训练1000步，那么前100步就是从lr=0慢慢长到参数设定的lr)
-    # "warmup_steps": 500,                 # 预热步数, 这个参数与warmup_ratio是矛盾的
-    "weight_decay": 0,                     # 权重衰减
-    "learning_rate": 5e-5,                 # 学习率
-    "max_grad_norm": 1.0,                  # 用于梯度裁剪，确保梯度的范数不超过1.0（默认1.0）
-    "fp16": False,                         # 是否使用16位浮点数进行训练（一般不推荐，loss很容易炸）
-    "bf16": False,                         # 是否使用16位宽浮点数进行训练（如果架构支持的话推荐使用）
-    "gradient_accumulation_steps": 2,      # 梯度累积步数，当batch size无法开很大时，可以考虑这个参数来实现大batch size的效果
-    "gradient_checkpointing": False,       # 当为True时，会在forward时适当丢弃一些中间量（用于backward），从而减轻显存压力（但会增加forward的时间）
-    "label_smoothing_factor": 0.0,         # softlabel，等于0时表示未开启
-    # "debug": "underflow_overflow",       # 训练时检查溢出，如果发生，则会发出警告。（该模式通常用于debug）
-    "jit_mode_eval": False,                 # 是否在eval的时候使用PyTorch jit trace（可以加速模型，但模型必须是静态的，否则会报错）
-    "torch_compile": False,                 # 是否使用torch.compile来编译模型（从而获得更好的训练和推理性能）
-                                           #+ 要求torch > 2.0，这个功能很好使，当模型跑通的时候可以开起来
-    # "deepspeed": "your_json_path",       #  使用deepspeed来训练，需要指定ds_config.json的路径
-                                           #+ 在Trainer中使用Deepspeed时一定要注意ds_config.json中的配置是否与Trainer的一致（如学习率，batch size，梯度累积步数等）
-                                           #+ 如果不一致，会出现很奇怪的bug（而且一般还很难发现）													
-
-    "dataloader_pin_memory": True,         # 可以加快数据在cpu和gpu之间转移的速度
-    "dataloader_num_workers": 16,          # 默认不会使用多进程来加载数据，通常设成4*所用的显卡数
-    "dataloader_drop_last": True,          # 丢掉最后一个minibatch，保证训练的梯度稳定
-
-    "evaluation_strategy": "steps",        # 评估策略，可以是"steps"或"epoch"
-    "eval_steps": 4000,                     # if evaluation_strategy="step"
-                                           #+默认情况下与logging_steps一样，可以是int也可以是(0~1)的float，当是float时表示总的训练步数的ratio(比方说可以设置成1.0 / 2000)
-
-    "save_strategy": "steps",              # 保存checkpoint的策略
-    "save_steps": 4000,                     # checkpoint保存的步数间隔，可以是int也可以是(0~1)的float，当是float时表示总的训练步数的ratio(比方说可以设置成1.0 / 2000)
-    "save_total_limit": 10,                 # 保存的模型的最大数量。如果超过这个数量，最旧的模型将被删除
-
-    "load_best_model_at_end": True,        # 训练结束时是否加载最佳模型
-                                           #+当设置True时，会保存训练时评估结果最好的checkpoint
-                                           #+当设置True时，evaluation_strategy必须与save_strategy一样，并且save_steps必须是eval_steps的整数倍
-    "metric_for_best_model": "eval_loss",  # 用于选择最佳模型的指标(必须与load_best_model_at_end一起用)
-                                           #+可以使用compute_metrics输出的evaluation的结果中（一个字典）的某个值
-                                           #+注意：Trainer会在compute_metrics输出的字典的键前面加上一个prefix，默认就是“eval_”
-    "greater_is_better": False,            # 指标值越小越好(必须与metric_for_best_model一起用)
-
-    "do_train": True,                      # 是否进行训练，通常用于调试
-    "do_eval": True,                       # 是否进行评估，通常用于调试
-
-    "remove_unused_columns": False,        # 是否删除没有用到的列（特征），默认为True
-                                           #+当删除了没用到的列后，making it easier to unpack inputs into the model’s call function
-    #+注意：remove_unused_columns去除列的操作会把传入的dataset的columns_names与模型forward方法中的参数名进行配对，对于不存在forward方法中的列名就会直接删掉整个feature
-    #+因此如果在dataset.with_transform(..)中给数据进行改名，那么这个remove操作会直接把原始的数据直接删掉，从而导致之后会拿到一个空的dataset，导致在对dataset进行切片取值时出问题
-    #+例如读进来的dataset图片对应的feature name叫"images"，而模型forward方法中对应的参数名叫“pixel_values”，
-    #+此时如果是在data.withtransfrom(..)中根据这个"images"生成其他模型forward方法中需要的参数，然后再把"images"改名成“pixel_values”，那么整个过程就会出问题
-    #+因为设置了remove_unused_columns=True后，会先给dataset进行列名检查，然后“images”这个feature会直接被删掉（导致with_transform的transform_fn拿不到“images”这个feature）
-    #+所以一个good practice就是：对于要改名的特征，先提前使用dataset.rename_column进行改名
-
-    "push_to_hub": False,                  # 是否训练完后上传hub，需要先在命令行：huggingface-cli login进行登录认证的配置，配置完后，认证信息会存到cache文件夹里
-}
--- a/src/models/ocr_model/utils/functional.py
+++ b/src/models/ocr_model/utils/functional.py
@@ -1,59 +0,0 @@
-import torch
-
-from transformers import DataCollatorForLanguageModeling
-from typing import List, Dict, Any
-from .transforms import train_transform, inference_transform
-from ...globals import MIN_HEIGHT, MIN_WIDTH, MAX_TOKEN_SIZE
-
-
-def left_move(x: torch.Tensor, pad_val):
-    assert len(x.shape) == 2, 'x should be 2-dimensional'
-    lefted_x = torch.ones_like(x)
-    lefted_x[:, :-1] = x[:, 1:]
-    lefted_x[:, -1] = pad_val
-    return lefted_x
-
-
-def tokenize_fn(samples: Dict[str, List[Any]], tokenizer=None) -> Dict[str, List[Any]]:
-    assert tokenizer is not None, 'tokenizer should not be None'
-    tokenized_formula = tokenizer(samples['latex_formula'], return_special_tokens_mask=True)
-    tokenized_formula['pixel_values'] = samples['image']
-    return tokenized_formula
-
-
-def collate_fn(samples: List[Dict[str, Any]], tokenizer=None) -> Dict[str, List[Any]]:
-    assert tokenizer is not None, 'tokenizer should not be None'
-    pixel_values = [dic.pop('pixel_values') for dic in samples]
-
-    clm_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
-    
-    batch = clm_collator(samples)
-    batch['pixel_values'] = pixel_values
-    batch['decoder_input_ids'] = batch.pop('input_ids')
-    batch['decoder_attention_mask'] = batch.pop('attention_mask')
-
-    # 左移labels和decoder_attention_mask
-    batch['labels'] = left_move(batch['labels'], -100)
-
-    # 把list of Image转成一个tensor with (B, C, H, W)
-    batch['pixel_values'] = torch.stack(batch['pixel_values'], dim=0)
-    return batch
-
-
-def img_train_transform(samples: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
-    processed_img = train_transform(samples['pixel_values'])
-    samples['pixel_values'] = processed_img
-    return samples
-
-
-def img_inf_transform(samples: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
-    processed_img = inference_transform(samples['pixel_values'])
-    samples['pixel_values'] = processed_img
-    return samples
-
-
-def filter_fn(sample, tokenizer=None) -> bool:
-    return (
-        sample['image'].height > MIN_HEIGHT and sample['image'].width > MIN_WIDTH
-        and len(tokenizer(sample['latex_formula'])['input_ids']) < MAX_TOKEN_SIZE - 10
-    )
--- a/src/models/ocr_model/utils/helpers.py
+++ b/src/models/ocr_model/utils/helpers.py
@@ -1,39 +0,0 @@
-import cv2
-import numpy as np
-from typing import List
-from PIL import Image
-
-
-def convert2rgb(image_paths: List[str]) -> List[np.ndarray]:
-    # 输出的np.ndarray的格式为：[H, W, C]（通道在第三维），通道的排列顺序为RGB
-    processed_images = []
-
-    for path in image_paths:
-        # 读取图片
-        image = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-
-        if image is None:
-            print(f"Image at {path} could not be read.")
-            continue
-
-        # 检查图片是否使用 uint16 类型
-        if image.dtype == np.uint16:
-            raise ValueError(f"Image at {path} is stored in uint16, which is not supported.")
-
-        # 获取图片通道数
-        channels = 1 if len(image.shape) == 2 else image.shape[2]
-
-        # 如果是 RGBA (4通道), 转换为 RGB
-        if channels == 4:
-            image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGB)
-
-        # 如果是 I 模式 (单通道灰度图), 转换为 RGB
-        elif channels == 1:
-            image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
-
-        # 如果是 BGR (3通道), 转换为 RGB
-        elif channels == 3:
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-        processed_images.append(image)
-
-    return processed_images
--- a/src/models/ocr_model/utils/inference.py
+++ b/src/models/ocr_model/utils/inference.py
@@ -1,39 +0,0 @@
-import torch
-
-from transformers import RobertaTokenizerFast, GenerationConfig
-from typing import List
-
-from models.ocr_model.model.TexTeller import TexTeller
-from models.ocr_model.utils.transforms import inference_transform
-from models.ocr_model.utils.helpers import convert2rgb
-from models.globals import MAX_TOKEN_SIZE
-
-
-def inference(
-    model: TexTeller, 
-    tokenizer: RobertaTokenizerFast,
-    imgs_path: List[str], 
-    use_cuda: bool,
-    num_beams: int = 1,
-) -> List[str]:
-    model.eval()
-    imgs = convert2rgb(imgs_path)
-    imgs = inference_transform(imgs)
-    pixel_values = torch.stack(imgs)
-
-    if use_cuda:
-        model = model.to('cuda')
-        pixel_values = pixel_values.to('cuda')
-
-
-    generate_config = GenerationConfig(
-        max_new_tokens=MAX_TOKEN_SIZE,
-        num_beams=num_beams,
-        do_sample=False,
-        pad_token_id=tokenizer.pad_token_id,
-        eos_token_id=tokenizer.eos_token_id,
-        bos_token_id=tokenizer.bos_token_id,
-    )
-    pred = model.generate(pixel_values, generation_config=generate_config)
-    res = tokenizer.batch_decode(pred, skip_special_tokens=True)
-    return res
--- a/src/models/ocr_model/utils/metrics.py
+++ b/src/models/ocr_model/utils/metrics.py
@@ -1,17 +0,0 @@
-import evaluate
-import numpy as np
-from transformers import EvalPrediction, RobertaTokenizer
-from typing import Dict
-
-def bleu_metric(eval_preds:EvalPrediction, tokenizer:RobertaTokenizer) -> Dict:
-    metric = evaluate.load('/home/lhy/code/TexTeller/src/models/ocr_model/train/google_bleu')  # 这里需要联网，所以会卡住
-    
-    logits, labels = eval_preds.predictions, eval_preds.label_ids
-    preds = logits
-    # preds = np.argmax(logits, axis=1)  # 把logits转成对应的预测标签
-
-    labels = np.where(labels == -100, 1, labels)
-
-    preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
-    labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
-    return metric.compute(predictions=preds, references=labels)
--- a/src/models/ocr_model/utils/transforms.py
+++ b/src/models/ocr_model/utils/transforms.py
@@ -1,221 +0,0 @@
-import torch
-import random
-import numpy as np
-import cv2
-
-from torchvision.transforms import v2
-from typing import List
-from PIL import Image
-
-from ...globals import (
-    OCR_IMG_CHANNELS,
-    OCR_IMG_SIZE,
-    OCR_FIX_SIZE,
-    IMAGE_MEAN, IMAGE_STD,
-    MAX_RESIZE_RATIO, MIN_RESIZE_RATIO
-)
-from .ocr_aug import ocr_augmentation_pipeline
-
-# train_pipeline = default_augraphy_pipeline(scan_only=True)
-train_pipeline = ocr_augmentation_pipeline()
-
-general_transform_pipeline = v2.Compose([
-    v2.ToImage(),    # Convert to tensor, only needed if you had a PIL image
-                     #+返回一个List of torchvision.Image，list的长度就是batch_size
-                     #+因此在整个Compose pipeline的最后，输出的也是一个List of torchvision.Image
-                     #+注意：不是返回一整个torchvision.Image，batch_size的维度是拿出来的
-    v2.ToDtype(torch.uint8, scale=True),  # optional, most input are already uint8 at this point
-    v2.Grayscale(),  # 转灰度图（视具体任务而定）
-
-    v2.Resize(       # 固定resize到一个正方形上
-        size=OCR_IMG_SIZE - 1,  # size必须小于max_size 
-        interpolation=v2.InterpolationMode.BICUBIC,
-        max_size=OCR_IMG_SIZE,
-        antialias=True
-    ),
-
-    v2.ToDtype(torch.float32, scale=True),  # Normalize expects float input
-    v2.Normalize(mean=[IMAGE_MEAN], std=[IMAGE_STD]),
-
-    # v2.ToPILImage()  # 用于观察转换后的结果是否正确（debug用）
-])
-
-
-def trim_white_border(image: np.ndarray):
-    # image是一个3维的ndarray，RGB格式，维度分布为[H, W, C]（通道维在第三维上）
-
-    # # 检查images中的第一个元素是否是嵌套的列表结构
-    # if isinstance(image, list):
-    #     image = np.array(image, dtype=np.uint8)
-
-    # 检查图像是否为RGB格式，同时检查通道维是不是在第三维上
-    if len(image.shape) != 3 or image.shape[2] != 3:
-        raise ValueError("Image is not in RGB format or channel is not in third dimension")
-
-    # 检查图片是否使用 uint8 类型
-    if image.dtype != np.uint8:
-        raise ValueError(f"Image should stored in uint8")
-
-    # 创建与原图像同样大小的纯白背景图像
-    h, w = image.shape[:2]
-    bg = np.full((h, w, 3), 255, dtype=np.uint8)
-
-    # 计算差异
-    diff = cv2.absdiff(image, bg)
-
-    # 只要差值大于1，就全部转化为255
-    _, diff = cv2.threshold(diff, 1, 255, cv2.THRESH_BINARY)
-
-    # 把差值转灰度图
-    gray_diff = cv2.cvtColor(diff, cv2.COLOR_RGB2GRAY)
-    # 计算图像中非零像素点的最小外接矩阵
-    x, y, w, h = cv2.boundingRect(gray_diff) 
-
-    # 裁剪图像
-    trimmed_image = image[y:y+h, x:x+w]
-
-    return trimmed_image
-
-
-def add_white_border(image: np.ndarray, max_size: int) -> np.ndarray:
-    randi = [random.randint(0, max_size) for _ in range(4)]
-    pad_height_size = randi[1] + randi[3]
-    pad_width_size  = randi[0] + randi[2]
-    if (pad_height_size + image.shape[0] < 30):
-        compensate_height = int((30 - (pad_height_size + image.shape[0])) * 0.5) + 1
-        randi[1] += compensate_height
-        randi[3] += compensate_height
-    if (pad_width_size + image.shape[1] < 30):
-        compensate_width = int((30 - (pad_width_size + image.shape[1])) * 0.5) + 1
-        randi[0] += compensate_width
-        randi[2] += compensate_width
-    return v2.functional.pad(
-        torch.from_numpy(image).permute(2, 0, 1),
-        padding=randi,
-        padding_mode='constant',
-        fill=(255, 255, 255)
-    )
-
-
-def padding(images: List[torch.Tensor], required_size: int) -> List[torch.Tensor]:
-    images = [  
-        v2.functional.pad(
-            img,
-            padding=[0, 0, required_size - img.shape[2], required_size - img.shape[1]]
-        )
-        for img in images
-    ]
-    return images
-
-
-def random_resize(
-    images: List[np.ndarray], 
-    minr: float, 
-    maxr: float
-) -> List[np.ndarray]:
-    # np.ndarray的格式：3维，RGB格式，维度分布为[H, W, C]（通道维在第三维上）
-
-    # # 检查images中的第一个元素是否是嵌套的列表结构
-    # if isinstance(images[0], list):
-    #     # 将嵌套的列表结构转换为np.ndarray
-    #     images = [np.array(img, dtype=np.uint8) for img in images]
-
-    if len(images[0].shape) != 3 or images[0].shape[2] != 3:
-        raise ValueError("Image is not in RGB format or channel is not in third dimension")
-
-    ratios = [random.uniform(minr, maxr) for _ in range(len(images))]
-    return [
-        cv2.resize(img, (int(img.shape[1] * r), int(img.shape[0] * r)), interpolation=cv2.INTER_LANCZOS4)  # 抗锯齿
-        for img, r in zip(images, ratios)
-    ]
-
-
-def rotate(image: np.ndarray, min_angle: int, max_angle: int) -> np.ndarray:
-    # Get the center of the image to define the point of rotation
-    image_center = tuple(np.array(image.shape[1::-1]) / 2)
-
-    # Generate a random angle within the specified range
-    angle = random.randint(min_angle, max_angle)
-
-    # Get the rotation matrix for rotating the image around its center
-    rotation_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
-
-    # Determine the size of the rotated image
-    cos = np.abs(rotation_mat[0, 0])
-    sin = np.abs(rotation_mat[0, 1])
-    new_width = int((image.shape[0] * sin) + (image.shape[1] * cos))
-    new_height = int((image.shape[0] * cos) + (image.shape[1] * sin))
-
-    # Adjust the rotation matrix to take into account translation
-    rotation_mat[0, 2] += (new_width / 2) - image_center[0]
-    rotation_mat[1, 2] += (new_height / 2) - image_center[1]
-
-    # Rotate the image with the specified border color (white in this case)
-    rotated_image = cv2.warpAffine(image, rotation_mat, (new_width, new_height), borderValue=(255, 255, 255))
-
-    return rotated_image
-
-
-def ocr_aug(image: np.ndarray) -> np.ndarray:
-    # 20%的概率进行随机旋转
-    if random.random() < 0.2:
-        image = rotate(image, -5, 5)
-    # 增加白边
-    image = add_white_border(image, max_size=25).permute(1, 2, 0).numpy()
-    # 数据增强
-    image = train_pipeline(image)
-    return image
-
-
-def train_transform(images: List[Image.Image]) -> List[torch.Tensor]:
-    assert OCR_IMG_CHANNELS == 1 , "Only support grayscale images for now"
-    assert OCR_FIX_SIZE == True, "Only support fixed size images for now"
-
-    images = [np.array(img.convert('RGB')) for img in images]
-    # random resize first
-    images = random_resize(images, MIN_RESIZE_RATIO, MAX_RESIZE_RATIO)
-    # 裁剪掉白边
-    images = [trim_white_border(image) for image in images]
-
-    # OCR augmentation
-    images = [ocr_aug(image) for image in images]
-
-    # general transform pipeline
-    images = [general_transform_pipeline(image) for image in  images]
-    # padding to fixed size
-    images = padding(images, OCR_IMG_SIZE)
-    return images
-
-
-def inference_transform(images: List[np.ndarray]) -> List[torch.Tensor]:
-    assert OCR_IMG_CHANNELS == 1 , "Only support grayscale images for now"
-    assert OCR_FIX_SIZE == True, "Only support fixed size images for now"
-    images = [np.array(img.convert('RGB')) for img in images]
-    # 裁剪掉白边
-    images = [trim_white_border(image) for image in images]
-    # general transform pipeline
-    images = [general_transform_pipeline(image) for image in  images]  # imgs: List[PIL.Image.Image]
-    # padding to fixed size
-    images = padding(images, OCR_IMG_SIZE)
-
-    return images
-
-
-if __name__ == '__main__':
-    from pathlib import Path
-    from .helpers import convert2rgb
-    base_dir = Path('/home/lhy/code/TeXify/src/models/ocr_model/model')
-    imgs_path = [
-        base_dir / '1.jpg',
-        base_dir / '2.jpg',
-        base_dir / '3.jpg',
-        base_dir / '4.jpg',
-        base_dir / '5.jpg',
-        base_dir / '6.jpg',
-        base_dir / '7.jpg',
-    ]
-    imgs_path = [str(img_path) for img_path in imgs_path]
-    imgs = convert2rgb(imgs_path)
-    res = random_resize(imgs, 0.5, 1.5)
-    pause = 1
-
--- a/src/models/resizer/inference.py
+++ b/src/models/resizer/inference.py
@@ -1,44 +0,0 @@
-#!/usr/bin/env python3
-import os
-import argparse
-import torch
-
-from pathlib import Path
-from PIL import Image
-from .model.Resizer import Resizer
-from .utils import preprocess_fn
-
-from munch import Munch
-
-
-def inference(args):
-    img = Image.open(args.image)
-    img = img.convert('RGB') if img.format == 'PNG' else img
-    processed_img = preprocess_fn({"pixel_values": [img]})
-
-    ckt_path = Path(args.checkpoint).resolve()
-    model = Resizer.from_pretrained(ckt_path)
-    model.eval()
-    inpu = torch.stack(processed_img['pixel_values'])
-    pred = model(inpu) * 1.25
-    print(pred)
-
-    ...
-
-
-if __name__ == "__main__":
-    cur_dirpath = os.getcwd()
-    script_dirpath = Path(__file__).resolve().parent
-    os.chdir(script_dirpath)
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-img', '--image', type=str, required=True)
-    parser.add_argument('-ckt', '--checkpoint', type=str, required=True)
-
-    args = parser.parse_args([
-        '-img', '/home/lhy/code/TeXify/src/models/resizer/foo5_140h.jpg',
-        '-ckt', '/home/lhy/code/TeXify/src/models/resizer/train/train_result_pred_height_v5'
-    ])
-    inference(args)
-
-    os.chdir(cur_dirpath)
--- a/src/models/resizer/model/Resizer.py
+++ b/src/models/resizer/model/Resizer.py
@@ -1,5 +0,0 @@
-from transformers import ResNetForImageClassification
-
-class Resizer(ResNetForImageClassification):
-    def __init__(self, config):
-        super().__init__(config)
--- a/src/models/resizer/train/train.py
+++ b/src/models/resizer/train/train.py
@@ -1,122 +0,0 @@
-import os
-import datasets
-
-from pathlib import Path
-from transformers import (
-    ResNetConfig,
-    TrainingArguments,
-    Trainer
-)
-
-from ..utils import preprocess_fn
-from ..model.Resizer import Resizer
-from ...globals import NUM_CHANNELS, NUM_CLASSES, RESIZER_IMG_SIZE
-
-
-def train():
-    cur_dirpath = os.getcwd()
-    script_dirpath = Path(__file__).resolve().parent
-    os.chdir(script_dirpath)
-
-    data = datasets.load_dataset("./dataset").shuffle(seed=42)
-    data = data.rename_column("images", "pixel_values")
-    data.flatten_indices()
-    data = data.with_transform(preprocess_fn)
-    train_data, test_data = data['train'], data['test']
-
-    config = ResNetConfig(
-        num_channels=NUM_CHANNELS,
-        num_labels=NUM_CLASSES,
-        img_size=RESIZER_IMG_SIZE
-    )
-    model = Resizer(config)
-    model = Resizer.from_pretrained("/home/lhy/code/TeXify/src/models/resizer/train/train_result_pred_height_v4/checkpoint-213000")
-
-    training_args = TrainingArguments(
-        # resume_from_checkpoint="/home/lhy/code/TeXify/src/models/resizer/train/train_result_pred_height_v3/checkpoint-94500",
-        max_grad_norm=1.0,
-        # use_cpu=True,
-        seed=42,                            # 随机种子，用于确保实验的可重复性
-        # data_seed=42,                     # data sampler的采样也固定
-        # full_determinism=True,            # 使整个训练完全固定（这个设置会有害于模型训练，只用于debug）
-
-        output_dir='./train_result_pred_height_v5',        # 输出目录
-        overwrite_output_dir=False,         # 如果输出目录存在，不删除原先的内容
-        report_to=["tensorboard"],          # 输出日志到TensorBoard，
-                                            #+通过在命令行：tensorboard --logdir ./logs 来查看日志
-
-        logging_dir=None,               # TensorBoard日志文件的存储目录
-        log_level="info",
-        logging_strategy="steps",           # 每隔一定步数记录一次日志
-        logging_steps=500,                  # 记录日志的步数间隔
-        logging_nan_inf_filter=False,       # 对loss=nan或inf进行记录
-
-        num_train_epochs=50,                 # 总的训练轮数
-        # max_steps=3,                      # 训练的最大步骤数。如果设置了这个参数，
-                                            #+那么num_train_epochs将被忽略（通常用于调试）
-
-        # label_names = ['your_label_name'],    # 指定data_loader中的标签名，如果不指定则默认为'labels'
-
-        per_device_train_batch_size=55,     # 每个GPU的batch size
-        per_device_eval_batch_size=48*2,      # 每个GPU的evaluation batch size
-        auto_find_batch_size=False,         # 自动搜索合适的batch size（指数decay）
-
-        optim = 'adamw_torch',              # 还提供了很多AdamW的变体（相较于经典的AdamW更加高效）
-                                            #+当设置了optim后，就不需要在Trainer中传入optimizer
-        lr_scheduler_type="cosine",         # 设置lr_scheduler
-        warmup_ratio=0.1,                   # warmup占整个训练steps的比例
-        # warmup_steps=500,                 # 预热步数
-        weight_decay=0,                     # 权重衰减
-        learning_rate=5e-5,                 # 学习率
-        fp16=False,                         # 是否使用16位浮点数进行训练
-        gradient_accumulation_steps=1,      # 梯度累积步数，当batch size无法开很大时，可以考虑这个参数来实现大batch size的效果
-        gradient_checkpointing=False,       # 当为True时，会在forward时适当丢弃一些中间量（用于backward），从而减轻显存压力（但会增加forward的时间）
-        label_smoothing_factor=0.0,         # softlabel，等于0时表示未开启
-        # debug='underflow_overflow',       # 训练时检查溢出，如果发生，则会发出警告。（该模式通常用于debug）
-        torch_compile=True,                # 是否使用torch.compile来编译模型（从而获得更好的训练和推理性能）
-                                            #+ 要求torch > 2.0，并且这个功能现在还不是很稳定
-        # deepspeed='your_json_path',       #  使用deepspeed来训练，需要指定ds_config.json的路径
-                                            #+ 在Trainer中使用Deepspeed时一定要注意ds_config.json中的配置是否与Trainer的一致（如学习率，batch size，梯度累积步数等）
-                                            #+ 如果不一致，会出现很奇怪的bug（而且一般还很难发现）													
-
-        dataloader_pin_memory=True,         # 可以加快数据在cpu和gpu之间转移的速度
-        dataloader_num_workers=16,           # 默认不会使用多进程来加载数据
-        dataloader_drop_last=True,          # 丢掉最后一个minibatch
-
-        evaluation_strategy="steps",        # 评估策略，可以是"steps"或"epoch"
-        eval_steps=500,                       # if evaluation_strategy="step"
-        # eval_steps=10,                     # if evaluation_strategy="step"
-
-        save_strategy="steps",              # 保存checkpoint的策略
-        save_steps=1500,                    # 模型保存的步数间隔
-        save_total_limit=5,                 # 保存的模型的最大数量。如果超过这个数量，最旧的模型将被删除
-
-        load_best_model_at_end=True,        # 训练结束时是否加载最佳模型
-        metric_for_best_model="eval_loss",  # 用于选择最佳模型的指标
-        greater_is_better=False,            # 指标值越小越好
-
-        do_train=True,                      # 是否进行训练，通常用于调试
-        do_eval=True,                       # 是否进行评估，通常用于调试
-
-        remove_unused_columns=True,         # 是否删除没有用到的列（特征），默认为True
-                                            #+当删除了没用到的列后，making it easier to unpack inputs into the model’s call function
-
-        push_to_hub=False,                  # 是否训练完后上传hub，需要先在命令行：huggingface-cli login进行登录认证的配置，配置完后，认证信息会存到cache文件夹里
-        hub_model_id="a_different_name",    # 模型的名字
-                                            #+每次保存模型时，都会上传到hub，
-                                            #+训练完后，记得trainer.push_to_hub()，会将模型使用的参数以及验证集上的结果传到hub上 
-    )
-
-    trainer = Trainer(
-        model,
-        training_args,
-        train_dataset=train_data,
-        eval_dataset=test_data,
-    )
-    trainer.train()
-
-    os.chdir(cur_dirpath)
-
-
-if __name__ == '__main__':
-    train()
--- a/src/models/resizer/utils/init.py
+++ b/src/models/resizer/utils/init.py
@@ -1 +0,0 @@
-from .preprocess import preprocess_fn
--- a/src/models/resizer/utils/preprocess.py
+++ b/src/models/resizer/utils/preprocess.py
@@ -1,75 +0,0 @@
-import torch
-from torchvision.transforms import v2
-
-from PIL import Image, ImageChops
-from ...globals import (
-    IMAGE_MEAN, IMAGE_STD, 
-    LABEL_RATIO,
-    RESIZER_IMG_SIZE,
-    NUM_CHANNELS
-)
-
-from typing import (
-    Any,
-    List,
-    Dict,
-)
-
-
-def trim_white_border(image: Image):
-    if image.mode == 'RGB':
-        bg_color = (255, 255, 255)
-    elif image.mode == 'RGBA':
-        bg_color = (255, 255, 255, 255)
-    elif image.mode == 'L':
-        bg_color = 255
-    else:
-        raise ValueError("Unsupported image mode")
-    bg = Image.new(image.mode, image.size, bg_color)
-    diff = ImageChops.difference(image, bg)
-    diff = ImageChops.add(diff, diff, 2.0, -100)
-    bbox = diff.getbbox()
-    if bbox:
-        return image.crop(bbox)
-
-
-def preprocess_fn(samples: Dict[str, List[Any]]) -> Dict[str, List[Any]]:
-    imgs = samples['pixel_values']
-    imgs = [trim_white_border(img) for img in imgs]
-    labels = [float(img.height * LABEL_RATIO) for img in imgs]
-
-    assert NUM_CHANNELS == 1, "Only support grayscale images"
-    transform = v2.Compose([
-        v2.ToImage(),
-        v2.ToDtype(torch.uint8, scale=True),
-        v2.Grayscale(),
-        v2.Resize(
-            size=RESIZER_IMG_SIZE - 1,  # size必须小于max_size 
-            interpolation=v2.InterpolationMode.BICUBIC,
-            max_size=RESIZER_IMG_SIZE,
-            antialias=True
-        ),
-        v2.ToDtype(torch.float32, scale=True),
-        v2.Normalize(mean=[IMAGE_MEAN], std=[IMAGE_STD]),
-    ])
-    imgs = transform(imgs)
-    imgs = [
-        v2.functional.pad(
-            img,
-            padding=[0, 0, RESIZER_IMG_SIZE - img.shape[2], RESIZER_IMG_SIZE - img.shape[1]]
-        )
-        for img in imgs
-    ]
-
-    res = {'pixel_values': imgs, 'labels': labels}
-    return res
-
-
-if __name__ == "__main__":  # unit test
-    import datasets
-    data = datasets.load_dataset("/home/lhy/code/TeXify/src/models/resizer/train/dataset/dataset.py").shuffle(seed=42)
-    data = data.with_transform(preprocess_fn)
-    train_data, test_data = data['train'], data['test']
-
-    inpu = train_data[:10]
-    pause = 1
--- a/src/models/tokenizer/roberta-tokenizer-550K/merges.txt
+++ b/src/models/tokenizer/roberta-tokenizer-550K/merges.txt
--- a/src/models/tokenizer/roberta-tokenizer-550K/special_tokens_map.json
+++ b/src/models/tokenizer/roberta-tokenizer-550K/special_tokens_map.json
@@ -1,15 +0,0 @@
-{
-  "bos_token": "<s>",
-  "cls_token": "<s>",
-  "eos_token": "</s>",
-  "mask_token": {
-    "content": "<mask>",
-    "lstrip": true,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": "<pad>",
-  "sep_token": "</s>",
-  "unk_token": "<unk>"
-}
--- a/src/models/tokenizer/roberta-tokenizer-550K/tokenizer.json
+++ b/src/models/tokenizer/roberta-tokenizer-550K/tokenizer.json
--- a/src/models/tokenizer/roberta-tokenizer-550K/tokenizer_config.json
+++ b/src/models/tokenizer/roberta-tokenizer-550K/tokenizer_config.json
@@ -1,57 +0,0 @@
-{
-  "add_prefix_space": false,
-  "added_tokens_decoder": {
-    "0": {
-      "content": "<s>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
-      "content": "<pad>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2": {
-      "content": "</s>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "3": {
-      "content": "<unk>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "4": {
-      "content": "<mask>",
-      "lstrip": true,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "bos_token": "<s>",
-  "clean_up_tokenization_spaces": true,
-  "cls_token": "<s>",
-  "eos_token": "</s>",
-  "errors": "replace",
-  "mask_token": "<mask>",
-  "model_max_length": 1000000000000000019884624838656,
-  "pad_token": "<pad>",
-  "sep_token": "</s>",
-  "tokenizer_class": "RobertaTokenizer",
-  "trim_offsets": true,
-  "unk_token": "<unk>"
-}
--- a/src/models/tokenizer/roberta-tokenizer-550K/vocab.json
+++ b/src/models/tokenizer/roberta-tokenizer-550K/vocab.json
--- a/src/models/tokenizer/roberta-tokenizer-7Mformulas/merges.txt
+++ b/src/models/tokenizer/roberta-tokenizer-7Mformulas/merges.txt
--- a/src/models/tokenizer/roberta-tokenizer-7Mformulas/special_tokens_map.json
+++ b/src/models/tokenizer/roberta-tokenizer-7Mformulas/special_tokens_map.json
@@ -1,15 +0,0 @@
-{
-  "bos_token": "<s>",
-  "cls_token": "<s>",
-  "eos_token": "</s>",
-  "mask_token": {
-    "content": "<mask>",
-    "lstrip": true,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": "<pad>",
-  "sep_token": "</s>",
-  "unk_token": "<unk>"
-}
--- a/src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer.json
+++ b/src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer.json
--- a/src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer_config.json
+++ b/src/models/tokenizer/roberta-tokenizer-7Mformulas/tokenizer_config.json
@@ -1,57 +0,0 @@
-{
-  "add_prefix_space": false,
-  "added_tokens_decoder": {
-    "0": {
-      "content": "<s>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
-      "content": "<pad>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2": {
-      "content": "</s>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "3": {
-      "content": "<unk>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "4": {
-      "content": "<mask>",
-      "lstrip": true,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "bos_token": "<s>",
-  "clean_up_tokenization_spaces": true,
-  "cls_token": "<s>",
-  "eos_token": "</s>",
-  "errors": "replace",
-  "mask_token": "<mask>",
-  "model_max_length": 1000000000000000019884624838656,
-  "pad_token": "<pad>",
-  "sep_token": "</s>",
-  "tokenizer_class": "RobertaTokenizer",
-  "trim_offsets": true,
-  "unk_token": "<unk>"
-}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
OleehyO	12e6bb4312	[deps] Pin transformers to 4.47	2025-04-21 12:24:03 +00:00
OleehyO	4292be86f2	[chore] Setup deps for doc build	2025-04-21 12:24:00 +00:00
OleehyO	3e0d236f6b	[chore] Update	2025-04-21 12:24:00 +00:00
OleehyO	b653b9e784	[CD] Add documentation auto-deployment	2025-04-21 12:23:56 +00:00
OleehyO	b85979b258	[deps] Add sphnix extension deps	2025-04-21 08:38:06 +00:00
OleehyO	05e494af4b	[docs] Fix typo	2025-04-21 08:21:16 +00:00
OleehyO	4b1b8d10de	[chore] Change logo font	2025-04-21 08:20:16 +00:00
OleehyO	c8e08a22aa	🔧 Fix all ruff typo errors & test CI/CD workflow (#109 ) * [chore] Fix ruff typo * [robot] Fix welcome robot	2025-04-21 13:52:16 +08:00
OleehyO	4d3be22956	[CI] Fix deps installation	2025-04-21 05:17:12 +00:00
OleehyO	4e92a38682	[CD] Change trigger condition	2025-04-21 05:12:38 +00:00
OleehyO	3e5272a476	[chore] Update README_zh.md	2025-04-21 05:11:47 +00:00
OleehyO	a329453873	[deps] Grouped deps & setup vcs	2025-04-21 04:48:29 +00:00
OleehyO	42db737ae6	[chore] Update README.md	2025-04-21 04:47:53 +00:00
OleehyO	29f6f8960d	[CI/CD] Setup complete workflow	2025-04-21 03:00:06 +00:00
OleehyO	6a67017962	[chore] Setup vcs and deps	2025-04-21 02:41:46 +00:00
OleehyO	d4a32094a7	[chore] Ignore images	2025-04-21 02:41:06 +00:00
OleehyO	3eceedd96b	[docs] Set up documentation structure with API reference	2025-04-21 02:38:36 +00:00
OleehyO	8aad53e079	Upload logo.svg	2025-04-21 02:37:28 +00:00
OleehyO	df03525fd7	[feat] Support dynamic package vcs	2025-04-21 02:36:13 +00:00
OleehyO	8a000edb7b	[docs] Add comprehensive function documentation	2025-04-21 02:34:56 +00:00
OleehyO	375ad4a4cb	Add globals test	2025-04-21 02:32:05 +00:00
OleehyO	59df576c85	[test] Init	2025-04-19 16:36:48 +00:00
OleehyO	d4cef5135f	[feat] Add texteller training script	2025-04-19 16:36:43 +00:00
OleehyO	25142c34f1	[CI] Update ruff hook	2025-04-19 14:32:32 +00:00
OleehyO	0cba17d9ce	[refactor] Init	2025-04-19 14:32:28 +00:00
OleehyO	e0cbf2c99f	[chore] Cleanup	2025-04-17 07:08:47 +00:00
OleehyO	4e2740ada0	[feat] Support n-gram stop criteria	2025-04-02 03:23:27 +00:00
OleehyO	509cb75dfa	[deps] Change onnx-gpu to manually install	2025-04-02 02:48:23 +00:00
三洋三洋	5673adecff	[feat][formatter] Integrate LaTeX formatter for improved formula readability - Add latex_formatter.py based on tex-fmt (https://github.com/WGUNDERWOOD/tex-fmt) - Update to_katex.py to use the new formatter - Enhance LaTeX formula output with better formatting and readability This integration helps make generated LaTeX formulas more readable and maintainable by applying consistent formatting rules.	2025-03-01 00:55:41 +08:00
三洋三洋	5cda58e8fc	[chore] Ignore ruff lint E741	2025-03-01 00:54:57 +08:00
三洋三洋	c35b0e9f53	[fix] Add project prefix	2025-02-28 23:38:12 +08:00
三洋三洋	f023c6741e	[feat] Remove bold style	2025-02-28 23:38:12 +08:00
三洋三洋	be9e32b439	[deps] Add ray serve & python-multipart	2025-02-28 23:37:53 +08:00
三洋三洋	cd9e4146e0	[chore] Add build system and pakage location	2025-02-28 23:18:06 +08:00
三洋三洋	3e9c6c00b8	[chore] Add python related rules	2025-02-28 23:18:03 +08:00
三洋三洋	0e5c5fd706	[chore] Remove unsed files	2025-02-28 20:54:51 +08:00
三洋三洋	4d3714bb4b	[chore] exclude paddleocr directory from pre-commit hooks	2025-02-28 20:01:54 +08:00
三洋三洋	3296077461	[chore] Setup project infrastructure	2025-02-28 20:01:52 +08:00
三洋三洋	a0942db712	[deps] pin transformers to 4.45.2 and sentence-transformers to 3.1.1	2025-02-01 13:00:44 +08:00
OleehyO	cee83611b5	Merge pull request #78 from OleehyO/pre_release Change to better import dependency	2024-08-07 12:43:15 +08:00
三洋三洋	e1046ba3fa	Change to better import dependency	2024-08-07 01:19:26 +08:00
OleehyO	bbc8ecf88b	Merge pull request #67 from OleehyO/pre_release Change setting name	2024-07-11 20:34:50 +08:00
三洋三洋	7438dee7ac	Change setting name	2024-07-11 20:33:51 +08:00
OleehyO	be922cc952	Merge pull request #60 from OleehyO/pre_release Pre release	2024-06-23 22:16:09 +08:00
三洋三洋	bfb1810fb0	Update README	2024-06-23 22:14:05 +08:00
三洋三洋	838febf48c	Remove onnxruntime-gpu	2024-06-23 22:13:51 +08:00
OleehyO	69f53d7256	Merge pull request #59 from OleehyO/pre_release Pre release	2024-06-22 23:56:45 +08:00
三洋三洋	6793142557	Update model config	2024-06-22 22:08:08 +08:00
三洋三洋	25f6cddf72	Update README	2024-06-22 22:00:14 +08:00
三洋三洋	cd519d8e99	Support onnx runtime	2024-06-22 22:00:05 +08:00
三洋三洋	2ae59776fa	Add optimum	2024-06-22 21:49:47 +08:00
OleehyO	529fba4db6	Merge pull request #58 from OleehyO/pre_release Add formula detection service	2024-06-17 21:26:35 +08:00
三洋三洋	d8659cd3a9	Add formula detection service	2024-06-17 21:23:55 +08:00
OleehyO	18dc6497ae	Merge pull request #56 from OleehyO/pre_release Add docker link	2024-06-11 13:22:17 +08:00
三洋三洋	c849728ee7	Add docker link	2024-06-11 13:20:32 +08:00
三洋三洋	a1c2b5b1ef	Update server.py 1. Change the default host address to 0.0.0.0. 2. Convert the output to KaTeX.	2024-06-07 12:26:24 +00:00
三洋三洋	6fbd285658	Update README	2024-06-07 06:54:23 +00:00
三洋三洋	9f4058c64b	Add Apache2.0 license	2024-06-06 13:06:16 +00:00
三洋三洋	236489ba2a	Add cover.png	2024-06-06 13:06:16 +00:00
三洋三洋	2920b753a8	Modify the names of options in the web.py Formula only -> Formula recognition Text formula mixed -> Paragraph recognition Improved display during mixed inference	2024-06-06 13:06:16 +00:00
三洋三洋	dbbec511ef	Refine mix_inference 1. Add the formula number back to the isolated formula and merge multiple tag. 2. remove bold effect from inline formuals 3. change split environment into aligned	2024-06-06 13:06:11 +00:00
三洋三洋	29e626c984	Bugfix: to_katex.py 1. Added `change_all` function to fix a bug where some LaTeX formulas with the same wrapper were causing issues. 2. Removed some unnecessary formatting commands. Bugfix: to_katex.py	2024-06-06 08:25:50 +00:00
三洋三洋	848726e6e2	Update	2024-05-28 09:51:53 +00:00
三洋三洋	e66f237cfd	Added releasing file	2024-05-28 07:50:09 +00:00
三洋三洋	f509b8c94a	Change the model configuration to trocr	2024-05-28 07:50:09 +00:00
三洋三洋	2ac159bfa2	Using paddleocr with onnxruntime Deleted the code for test time.	2024-05-28 07:50:09 +00:00
三洋三洋	226c1e1f76	Added mixed recognition change suryaocr to paddleocr	2024-05-28 07:50:08 +00:00
三洋三洋	a24ccd53ae	Added ONNX file for PaddleOCR model	2024-05-28 07:50:08 +00:00
三洋三洋	d3451d0ce7	Update .gitignore	2024-05-28 07:50:08 +00:00
三洋三洋	e2bf22dac8	Added code for PaddleOCR inference	2024-05-28 07:50:08 +00:00
三洋三洋	5c9cff2125	Eliminated dependency on paddleocr Change to trocr	2024-05-28 07:50:08 +00:00
三洋三洋	cc602f5a82	update	2024-05-28 07:50:08 +00:00
OleehyO	19827f1837	bugfix: ocr_aug.py Change "lhy_custom" in ink_swap_color to "random"	2024-05-28 07:49:55 +00:00
三洋三洋	0a51bde1c5	bugfix: missing filter_fn and inference/train transform	2024-05-12 07:49:04 +00:00
三洋三洋	249a4d5a5f	update	2024-05-12 07:47:35 +00:00
三洋三洋	720795e478	update	2024-05-10 03:48:31 +00:00
TonyLee1256	fac1cfdcda	Update requirements.txt	2024-05-09 00:23:32 +08:00
TonyLee1256	82f3eb67b7	Update mix_inference.py 替换文本OCR模型为paddleocr	2024-05-09 00:23:02 +08:00
TonyLee1256	30fbc6dc2d	Update inference.py 替换文本OCR模型为paddleocr	2024-05-09 00:22:01 +08:00
TonyLee1256	b869122dc6	Update inference.py 增加了计时功能	2024-05-09 00:20:32 +08:00
TonyLee1256	eaed8d88ca	Update infer_det.py 增加使用gpu进行onnx模型推理的功能	2024-05-09 00:19:39 +08:00
三洋三洋	5d95d2e65c	bugfix	2024-05-08 14:34:01 +00:00
三洋三洋	ad84fcfce8	Added Language option in mixed mode	2024-05-07 07:44:24 +00:00
三洋三洋	ec90b2fdb9	Update README	2024-05-07 07:30:29 +00:00
三洋三洋	ff1872d067	bugfix	2024-05-07 07:11:34 +00:00
三洋三洋	ef529f9234	Add train_config.yaml	2024-05-07 07:11:05 +00:00
三洋三洋	3c0ec95b26	update .gitignore	2024-05-07 06:54:53 +00:00
TonyLee1256	f3148ef32c	bugfix inference.py	2024-05-07 13:28:07 +08:00
TonyLee1256	3b18667541	Update README_zh.md	2024-05-07 13:27:23 +08:00
TonyLee1256	91efec1bfa	Update README.md	2024-05-07 13:26:50 +08:00
TonyLee1256	6aa4c49d33	Update README.md	2024-05-07 13:25:28 +08:00
TonyLee1256	7b2b947c47	bugfix inference.py	2024-05-07 13:19:43 +08:00
三洋三洋	a3b85c0d3d	update	2024-05-02 09:10:21 +00:00
三洋三洋	683e53c78d	Merge remote-tracking branch 'origin/pre_release' into pre_release	2024-04-21 16:13:49 +00:00
三洋三洋	cb02bc4313	update README.md	2024-04-21 16:13:45 +00:00
TonyLee1256	9e2d4347b1	Update rec_infer_from_crop_imgs.py	2024-04-22 00:08:36 +08:00
TonyLee1256	55823256ec	Update infer_det.py	2024-04-22 00:07:41 +08:00
TonyLee1256	58e565e2da	Update README.md	2024-04-21 22:14:23 +08:00
TonyLee1256	0de36b5523	Update README.md	2024-04-21 22:12:22 +08:00
TonyLee1256	7c50ae8595	Update README_zh.md	2024-04-21 22:09:58 +08:00
三洋三洋	dc57872bc9	Merge branch 'dev' into pre_release	2024-04-21 13:14:49 +00:00
三洋三洋	1997145cf6	Update README.md	2024-04-21 13:06:01 +00:00
三洋三洋	5f62c7fbf0	1) 修复了to_katex.py的bug; 2)把Box.py中的转化结果写在logs	2024-04-21 12:09:26 +00:00
三洋三洋	9b7e392c66	merge dev后调整了项目结构	2024-04-21 00:48:24 +08:00
三洋三洋	eac7f455d6	merge dev后删除了resizer	2024-04-21 00:13:21 +08:00
三洋三洋	f84168a00b	1) 实现了文本-公式混排识别; 2) 重构了项目结构	2024-04-21 00:05:14 +08:00
三洋三洋	3746ddd427	update infer_det.py	2024-04-18 00:06:05 +08:00
三洋三洋	d5eca45fcc	为了支持mixed inference, 重构了目录	2024-04-17 15:24:06 +00:00
三洋三洋	5a9138026f	修复了merge pre_release分支后导致参数名不一致的bug	2024-04-17 14:47:58 +00:00
三洋三洋	891a9c310a	Merge branch 'pre_release' into dev	2024-04-17 10:32:22 +00:00
三洋三洋	7a8491b595	checkpoint	2024-04-17 10:20:15 +00:00
三洋三洋	fe273c0258	update README.md	2024-04-17 10:08:46 +00:00
三洋三洋	b4b9e8cfc4	前端更新, inference.py更新 1) 前端支持剪贴板粘贴图片. 2) 前端支持模型配置. 3) 修改了inference.py的接口. 4) 删除了不必要的文件	2024-04-17 09:36:40 +00:00
三洋三洋	8e657bdc25	add contributor	2024-04-12 07:29:36 +00:00
三洋三洋	d80d7262ef	update README	2024-04-12 06:16:37 +00:00
三洋三洋	7d237d820c	work in progress	2024-04-12 03:20:04 +00:00
OleehyO	468f5c7a66	Merge pull request #14 from TonyLee1256/pre_release 新增公式检测模块	2024-04-12 00:46:45 +08:00
TonyLee1256	936744ea13	新增公式检测模块	2024-04-11 16:44:19 +00:00
三洋三洋	574dcc2842	修改了transforms.py中inference_transform的bug: 在训练的eval阶段没有把png图片转化为np.ndarray	2024-04-11 07:04:58 +00:00
三洋三洋	5c58b88c96	优化了transform.py中的trim_white_border	2024-04-10 16:09:13 +00:00
三洋三洋	aaee57acd2	增加了数据增强的概率	2024-04-09 13:50:35 +00:00
三洋三洋	7e163928c7	inference.py支持katex语法	2024-04-06 12:06:08 +00:00
三洋三洋	8fdaef43f9	update README.md	2024-04-06 11:57:50 +00:00
三洋三洋	35bc4e71a1	inference.py支持katex	2024-04-06 11:38:59 +00:00
三洋三洋	09f02166db	update README.md	2024-04-06 07:43:03 +00:00
三洋三洋	6179cc3226	web demo支持katex, 不再需要本地安装xelatex渲染器	2024-04-06 07:28:46 +00:00
三洋三洋	8d1e719455	web demo加入了katex支持, 不再需要本地安装xelatex渲染器	2024-04-06 07:18:40 +00:00
三洋三洋	dd00e11a98	inference_transform bugfix	2024-04-06 05:09:50 +00:00
三洋三洋	4d494520f8	完成了v3版本：加入自然场景的数据增强	2024-04-05 08:11:06 +00:00
三洋三洋	e99ca14d59	Merge remote-tracking branch 'origin/dev' into dev	2024-04-05 08:00:11 +00:00
三洋三洋	af34ac5552	Merge remote-tracking branch 'origin/dev' into dev	2024-04-05 07:52:40 +00:00
三洋三洋	34ac31504a	修改了v3(支持自然场景、混合文字场景识别)版本的inference.py模版	2024-04-05 07:27:07 +00:00
三洋三洋	5b730329b4	update README.md	2024-04-05 05:19:27 +00:00
三洋三洋	d8ee5e3b11	Merge remote-tracking branch 'origin/dev' into dev	2024-03-28 14:33:46 +00:00
三洋三洋	17c92cce37	merge v3_nature_scence	2024-03-28 14:33:25 +00:00
三洋三洋	bf220c1f7f	merge v3_nature_scence	2024-03-28 14:22:23 +00:00
三洋三洋	5b66e42df7	Merge remote-tracking branch 'origin/dev' into dev	2024-03-28 13:28:47 +00:00
三洋三洋	979301a768	TexTellerv2 release	2024-03-25 13:22:11 +00:00
OleehyO	14b637cd6b	Update README_zh.md	2024-03-25 16:35:34 +08:00
OleehyO	b64e119093	Update README_zh.md	2024-03-25 16:35:34 +08:00
OleehyO	c66b55638f	Update README.md	2024-03-25 16:34:46 +08:00
三洋三洋	3f4b3c9645	update	2024-03-25 08:32:17 +00:00
三洋三洋	5e191ff0fe	update	2024-03-25 07:53:11 +00:00
三洋三洋	9c3bb1c22a	update mp4	2024-03-25 07:32:33 +00:00
三洋三洋	ef218d67f6	TexTeller v2	2024-03-25 07:11:10 +00:00
三洋三洋	74341c7e8a	update	2024-03-19 14:43:03 +00:00
三洋三洋	5d089b5a7f	update	2024-03-03 12:09:14 +08:00
三洋三洋	d9ee6b0d9e	update	2024-03-01 22:42:15 +08:00
三洋三洋	2d21d2d215	update	2024-02-27 07:44:35 +00:00
三洋三洋	3527a4af47	updated API usage (supports remote calls)	2024-02-27 07:13:36 +00:00
三洋三洋	b4537944d0	Update README_zh.md	2024-02-12 16:33:49 +00:00
三洋三洋	72a60f8611	Update README	2024-02-12 16:27:58 +00:00
三洋三洋	3683623925	Update README_zh.md	2024-02-12 15:02:31 +00:00
三洋三洋	94b0781d84	Update README	2024-02-12 11:46:26 +00:00
三洋三洋	9bc165f955	Update files	2024-02-12 11:40:51 +00:00
三洋三洋	fa6bcda721	update README	2024-02-12 08:44:45 +00:00
三洋三洋	6e2e45a8d6	update README	2024-02-12 08:41:33 +00:00
三洋三洋	b4962bfa98	Initial commit	2024-02-11 10:44:42 +00:00
三洋三洋	f057490bdb	Initial commit	2024-02-11 09:14:40 +00:00