TexTeller is an end-to-end formula recognition model based on [TrOCR](https://arxiv.org/abs/2109.10282), 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).
>
> Additionally, if you find this project helpful, please don't forget to give it a star⭐️🙏️
- 📮[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:
- 📮[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.
As demonstrated in the video, TexTeller is also capable of recognizing entire text paragraphs. Although TexTeller has general text OCR capabilities, we still recommend using paragraph recognition for better results:
1. [Download the weights](https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco.onnx?download=true) of the formula detection model to the`src/models/det_model/model/`directory
TexTeller uses the lightweight [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) model by default for recognizing both Chinese and English text. You can try using a larger model to achieve better recognition results for both Chinese and English:
Place the weights of the recognition/detection model in the `det/` or `rec/` directories within `src/models/third_party/paddleocr/checkpoints/`, and rename them to `default_model.onnx`.
TexTeller’s formula detection model is trained on 3,415 images of Chinese educational materials (with over 130 layouts) and 8,272 images from the [IBEM dataset](https://zenodo.org/records/4757865), and it supports formula detection across entire images.
1. Download the model weights and place them in `src/models/det_model/model/` [[link](https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco.onnx?download=true)].
2. Run the following command in the `src/` directory, and the results will be saved in `src/subimages/`
<details>
<summary>Advanced: batch formula recognition</summary>
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 `src/results/`.
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 `src/` directory and then run the following command:
| `--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) |
We provide an example dataset in the `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`.
If you are using a different dataset, you might need to retrain the tokenizer to obtain a different vocabulary. After configuring your dataset, you can train your own tokenizer with the following command:
You can set your own tokenizer and checkpoint paths in `src/models/ocr_model/train/train.py` (refer to `train.py` for more information). If you are using the same architecture and vocabulary as TexTeller, you can also fine-tune TexTeller's default weights with your own dataset.
> 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.
