TexTeller/README.md

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    <h1>
        <img src="./assets/fire.svg" width=30, height=30> 
        𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛
        <img src="./assets/fire.svg" width=30, height=30>
    </h1>
    <p align="center">
        English | <a href="./assets/README_zh.md">中文</a>
    </p>
    <!-- <p align="center">
        <img src="./assets/web_demo.gif" alt="TexTeller_demo" width=800>
    </p> -->
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https://github.com/OleehyO/TexTeller/assets/56267907/b23b2b2e-a663-4abb-b013-bd47238d513b

TexTeller is an end-to-end formula recognition model based on ViT, capable of converting images into corresponding LaTeX formulas.

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**).

> ~~We will soon release a TexTeller checkpoint trained on a 7.5M dataset~~

## 🔄 Change Log

* 📮[2024-03-24] 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.
    > [There](./assets/test.pdf) are more test images here and a horizontal comparison of recognition models from different companies.

## 🔑 Prerequisites

python=3.10

pytorch

> [!WARNING]
> Only CUDA versions >= 12.0 have been fully tested, so it is recommended to use CUDA version >= 12.0

## 🖼 About Rendering LaTeX as Images

* **Install XeLaTex** and ensure `xelatex` can be called directly from the command line.

* To ensure correct rendering of the predicted formulas, **include the following packages** in your `.tex` file:

    ```tex
    \usepackage{multirow,multicol,amsmath,amsfonts,amssymb,mathtools,bm,mathrsfs,wasysym,amsbsy,upgreek,mathalfa,stmaryrd,mathrsfs,dsfont,amsthm,amsmath,multirow}
    ```

## 🚀 Getting Started

1. Clone the repository:

    ```bash
    git clone https://github.com/OleehyO/TexTeller
    ```

2. After [installing pytorch](https://pytorch.org/get-started/locally/#start-locally), install the project's dependencies:

    ```bash
    pip install -r requirements.txt
    ```

3. Enter the `TexTeller/src` directory and run the following command in the terminal to start inference:

    ```bash
    python inference.py -img "/path/to/image.{jpg,png}" 
    # use -cuda option to enable GPU inference
    #+e.g. python inference.py -img "./img.jpg" -cuda
    ```

> [!NOTE]
> The first time you run it, the required checkpoints will be downloaded from Hugging Face

## 🌐 Web Demo

To start the web demo, you need to first enter the `TexTeller/src` directory, then run the following command

```bash
./start_web.sh
```

Then, enter `http://localhost:8501` in your browser to see the web demo

> [!TIP]
> 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

> [!IMPORTANT]
> If you want to directly render the prediction results as images on the web (for example, to check if the prediction is correct), you need to ensure [xelatex is correctly installed](https://github.com/OleehyO/TexTeller?tab=readme-ov-file#Rendering-Predicted-Results)

## 📡 API Usage

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:

```bash
python server.py  # default settings
```

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):

| 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*.|
| `-port` | The server's service port, *default is 8000*. |
| `--use_gpu` | Whether to use GPU for inference, *default is CPU*. |
| `--num_beams` | The number of beams for beam search, *default is 1*. |
| `--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) |

> [!NOTE]
> A client demo can be found at `TexTeller/client/demo.py`, you can refer to `demo.py` to send requests to the server

## 🏋️‍♂️ Training

### Dataset

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`.

After preparing your dataset, you need to **change the `DIR_URL` variable to your own dataset's path** in `.../dataset/loader.py`

### Retraining the Tokenizer

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:

1. In `TexTeller/src/models/tokenizer/train.py`, change `new_tokenizer.save_pretrained('./your_dir_name')` to your custom output directory
    > 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`

2. **In the `TexTeller/src` directory**, run the following command:

    ```bash
    python -m models.tokenizer.train
    ```

### Training the Model

To train the model, you need to run the following command in the `TexTeller/src` directory:

```bash
python -m models.ocr_model.train.train
```

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.

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.

> [!NOTE]
> 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.

## 🚧 Limitations

* Does not support scanned images and PDF document recognition

* Does not support handwritten formulas

## 📅 Plans

- [x] ~~Train the model with a larger dataset (7.5M samples, coming soon)~~

- [ ] Recognition of scanned images

- [ ] PDF document recognition + Support for English and Chinese scenarios

- [ ] Inference acceleration

- [ ] ...

## 💖 Acknowledgments

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.

## ⭐️ Stargazers over time

[![Stargazers over time](https://starchart.cc/OleehyO/TexTeller.svg?variant=adaptive)](https://starchart.cc/OleehyO/TexTeller)