204 lines
8.8 KiB
Markdown
204 lines
8.8 KiB
Markdown
📄 English | <a href="./assets/README_zh.md">中文</a>
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<div align="center">
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<h1>
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<img src="./assets/fire.svg" width=30, height=30>
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𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛
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<img src="./assets/fire.svg" width=30, height=30>
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</h1>
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<p align="center">
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🤗 <a href="https://huggingface.co/OleehyO/TexTeller"> Hugging Face</a>
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</p>
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<!-- <p align="center">
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<img src="./assets/web_demo.gif" alt="TexTeller_demo" width=800>
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</p> -->
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</div>
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https://github.com/OleehyO/TexTeller/assets/56267907/b23b2b2e-a663-4abb-b013-bd47238d513b
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TexTeller is an end-to-end formula recognition model based on ViT, capable of converting images into corresponding LaTeX formulas.
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TexTeller was trained with ~~550K~~7.5M image-formula pairs (dataset available [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 (**except for scanned images and handwritten formulas**).
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> ~~We will soon release a TexTeller checkpoint trained on a 7.5M dataset~~
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## 🔄 Change Log
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* 📮[2024-03-25] TexTeller 2.0 released! The training data for TexTeller 2.0 has been increased to 7.5M (about **15 times more** than TexTeller 1.0 and also improved in data quality). The trained TexTeller 2.0 demonstrated **superior performance** in the test set, especially in recognizing rare symbols, complex multi-line formulas, and matrices.
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> [There](./assets/test.pdf) are more test images here and a horizontal comparison of recognition models from different companies.
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* 📮[2024-04-12] Trained a **formula detection model**, thereby enhancing the capability to detect and recognize formulas in entire documents (whole-image inference)!
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## 🔑 Prerequisites
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python=3.10
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[pytorch](https://pytorch.org/get-started/locally/)
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> [!WARNING]
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> Only CUDA versions >= 12.0 have been fully tested, so it is recommended to use CUDA version >= 12.0
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## 🚀 Getting Started
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1. Clone the repository:
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```bash
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git clone https://github.com/OleehyO/TexTeller
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```
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2. [Installing pytorch](https://pytorch.org/get-started/locally/#start-locally)
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3. Install the project's dependencies:
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```bash
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pip install -r requirements.txt
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```
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4. Enter the `TexTeller/src` directory and run the following command in the terminal to start inference:
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```bash
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python inference.py -img "/path/to/image.{jpg,png}"
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# use -cuda option to enable GPU inference
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#+e.g. python inference.py -img "./img.jpg" -cuda
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```
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> [!NOTE]
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> The first time you run it, the required checkpoints will be downloaded from Hugging Face
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## 🌐 Web Demo
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Go to the `TexTeller/src` directory and run the following command:
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```bash
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./start_web.sh
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```
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Enter `http://localhost:8501` in a browser to view the web demo.
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> [!TIP]
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> You can change the default configuration of `start_web.sh`, for example, to use GPU for inference (e.g. `USE_CUDA=True`) or to increase the number of beams (e.g. `NUM_BEAM=3`) to achieve higher accuracy.
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> [!NOTE]
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> If you are Windows user, please run the `start_web.bat` file instead.
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## 🧠 Full Image Inference
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TexTeller also supports **formula detection and recognition** on full images, allowing for the detection of formulas throughout the image, followed by batch recognition of the formulas.
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### Download Weights
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English documentation formula detection [[link](https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco_trained_on_IBEM_en_papers.onnx?download=true)]: Trained on 8272 images from the [IBEM dataset](https://zenodo.org/records/4757865).
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Chinese documentation formula detection [[link](https://huggingface.co/TonyLee1256/texteller_det/blob/main/rtdetr_r50vd_6x_coco_trained_on_cn_textbook.onnx)]: Trained on 2560 Chinese textbook images (100+ layouts).
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### Formula Detection
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Run the following command in the `TexTeller/src` directory:
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```bash
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python infer_det.py
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```
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Detects all formulas in the full image, and the results are saved in `TexTeller/src/subimages`.
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<div align="center">
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<img src="./assets/det_rec.png" width=400>
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</div>
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### Batch Formula Recognition
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After **formula detection**, run the following command in the `TexTeller/src` directory:
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```shell
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rec_infer_from_crop_imgs.py
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```
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This will use the results of the previous formula detection to perform batch recognition on all cropped formulas, saving the recognition results as txt files in `TexTeller/src/results`.
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## 📡 API Usage
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We use [ray serve](https://github.com/ray-project/ray) to provide an API interface for TexTeller, allowing you to integrate TexTeller into your own projects. To start the server, you first need to enter the `TexTeller/src` directory and then run the following command:
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```bash
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python server.py # default settings
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```
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You can pass the following arguments to `server.py` to change the server's inference settings (e.g. `python server.py --use_gpu` to enable GPU inference):
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| Parameter | Description |
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| --- | --- |
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| `-ckpt` | The path to the weights file, *default is TexTeller's pretrained weights*.|
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| `-tknz` | The path to the tokenizer, *default is TexTeller's tokenizer*.|
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| `-port` | The server's service port, *default is 8000*. |
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| `--use_gpu` | Whether to use GPU for inference, *default is CPU*. |
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| `--num_beams` | The number of beams for beam search, *default is 1*. |
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| `--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.|
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| `--ncpu_per_replica` | The number of CPU cores used per service replica, *default is 1*. |
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| `--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) |
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> [!NOTE]
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> A client demo can be found at `TexTeller/client/demo.py`, you can refer to `demo.py` to send requests to the server
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## 🏋️♂️ Training
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### Dataset
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We provide an example dataset in the `TexTeller/src/models/ocr_model/train/dataset` directory, you can place your own images in the `images` directory and annotate each image with its corresponding formula in `formulas.jsonl`.
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After preparing your dataset, you need to **change the `DIR_URL` variable to your own dataset's path** in `.../dataset/loader.py`
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### Retraining the Tokenizer
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If you are using a different dataset, you might need to retrain the tokenizer to obtain a different dictionary. After configuring your dataset, you can train your own tokenizer with the following command:
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1. In `TexTeller/src/models/tokenizer/train.py`, change `new_tokenizer.save_pretrained('./your_dir_name')` to your custom output directory
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> If you want to use a different dictionary size (default is 10k tokens), you need to change the `VOCAB_SIZE` variable in `TexTeller/src/models/globals.py`
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2. **In the `TexTeller/src` directory**, run the following command:
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```bash
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python -m models.tokenizer.train
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```
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### Training the Model
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To train the model, you need to run the following command in the `TexTeller/src` directory:
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```bash
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python -m models.ocr_model.train.train
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```
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You can set your own tokenizer and checkpoint paths in `TexTeller/src/models/ocr_model/train/train.py` (refer to `train.py` for more information). If you are using the same architecture and dictionary as TexTeller, you can also fine-tune TexTeller's default weights with your own dataset.
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In `TexTeller/src/globals.py` and `TexTeller/src/models/ocr_model/train/train_args.py`, you can change the model's architecture and training hyperparameters.
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> [!NOTE]
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> Our training scripts use the [Hugging Face Transformers](https://github.com/huggingface/transformers) library, so you can refer to their [documentation](https://huggingface.co/docs/transformers/v4.32.1/main_classes/trainer#transformers.TrainingArguments) for more details and configurations on training parameters.
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## 🚧 Limitations
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* Does not support scanned images and PDF document recognition
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* Does not support handwritten formulas
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## 📅 Plans
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- [x] ~~Train the model with a larger dataset (7.5M samples, coming soon)~~
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- [ ] Recognition of scanned images
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- [ ] PDF document recognition + Support for English and Chinese scenarios
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- [ ] Inference acceleration
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- [ ] ...
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## 💖 Acknowledgments
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Thanks to [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) which has brought me a lot of inspiration, and [im2latex-100K](https://zenodo.org/records/56198#.V2px0jXT6eA) which enriches our dataset.
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## ⭐️ Stargazers over time
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[](https://starchart.cc/OleehyO/TexTeller)
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