📄 English | 中文
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 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**).
## 🔄 Change Log
* 📮[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.
> [There](./assets/test.pdf) are more test images here and a horizontal comparison of recognition models from different companies.
* 📮[2024-04-12] Trained a **formula detection model**, thereby enhancing the capability to detect and recognize formulas in entire documents (whole-image inference)!
* 📮[2024-05-02] Support **mixed Chinese English formula recognition**.
## 🔑 Prerequisites
python=3.10
[pytorch](https://pytorch.org/get-started/locally/)
> Only CUDA versions >= 12.0 have been fully tested, so it is recommended to use CUDA version >= 12.0
## 🚀 Getting Started
1. Clone the repository:
```bash
git clone https://github.com/OleehyO/TexTeller
```
2. [Installing pytorch](https://pytorch.org/get-started/locally/#start-locally)
3. Install the project's dependencies:
```bash
pip install -r requirements.txt
```
4. 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 --inference-mode option to enable GPU(cuda or mps) inference
#+e.g. python inference.py -img "./img.jpg" --inference-mode cuda
# use -mix option to enable mixed text and formula recognition
#+e.g. python inferene.py -img "./img.jpg" -mix -lang "en"
```
> The first time you run it, the required checkpoints will be downloaded from Hugging Face
>[!IMPORTANT]
>If using mixed text and formula recognition, it is necessary to [download formula detection model weights](https://github.com/OleehyO/TexTeller?tab=readme-ov-file#download-weights)
## 🌐 Web Demo
Go to the `TexTeller/src` directory and run the following command:
```bash
./start_web.sh
```
Enter `http://localhost:8501` in a browser to view the web demo.
> [!NOTE]
> If you are Windows user, please run the `start_web.bat` file instead.
## 🧠 Full Image Inference
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.
### Download Weights
Download the model weights from [this link](https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco.onnx?download=true) and place them in `src/models/det_model/model`.
> TexTeller's formula detection model was trained on a total of 11,867 images, consisting of 3,415 images from Chinese textbooks (over 130 layouts) and 8,272 images from the [IBEM dataset](https://zenodo.org/records/4757865).
### Formula Detection
Run the following command in the `TexTeller/src` directory:
```bash
python infer_det.py
```
Detects all formulas in the full image, and the results are saved in `TexTeller/src/subimages`.
### Batch Formula Recognition
After **formula detection**, run the following command in the `TexTeller/src` directory:
```shell
python rec_infer_from_crop_imgs.py
```
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`.
## 📡 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
```
| 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*. |
| `--inference-mode` | Whether to use GPU(cuda or mps) 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
- [ ] ...
## ⭐️ Stargazers over time
[](https://starchart.cc/OleehyO/TexTeller)
## 💖 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.
## 👥 Contributors
𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛
