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abhinand/GOT-OCR-2.0-unofficial

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Note: ORIGINAL MODEL REPO: https://github.com/Ucas-HaoranWei/GOT-OCR2.0

<h3><a href="">General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model</a></h3>

<a href="https://github.com/Ucas-HaoranWei/GOT-OCR2.0/"><img src="https://img.shields.io/badge/Project-Page-Green"></a> <a href="https://arxiv.org/abs/2409.01704"><img src="https://img.shields.io/badge/Paper-PDF-orange"></a> <a href="https://github.com/Ucas-HaoranWei/GOT-OCR2.0/blob/main/assets/wechat.jpg"><img src="https://img.shields.io/badge/Wechat-blue"></a> <a href="https://zhuanlan.zhihu.com/p/718163422"><img src="https://img.shields.io/badge/zhihu-red"></a>

Haoran Wei*, Chenglong Liu*, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, Zheng Ge, Liang Zhao, Jianjian Sun, Yuang Peng, Chunrui Han, Xiangyu Zhang

<p align="center"> <img src="assets/got_logo.png" style="width: 200px" align=center> </p>

Release

  • —[2024/9/03]🔥🔥🔥 We open-source the codes, weights, and benchmarks. The paper can be found in this repo. We also have submitted it to Arxiv.
  • —[2024/9/03]🔥🔥🔥 We release the OCR-2.0 model GOT!

![Code License](https://github.com/tatsu-lab/stanfordalpaca/blob/main/LICENSE) [![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)](https://github.com/tatsu-lab/stanfordalpaca/blob/main/DATA_LICENSE)

Usage and License Notices: The data, code, and checkpoint are intended and licensed for research use only. They are also restricted to use that follow the license agreement of Vary.

Community contributions

We encourage everyone to develop GOT applications based on this repo. Thanks for the following contributions :

Colab of GOT ~ contributor： @Zizhe Wang

Contents

<p align="center"> <img src="assets/got_support.jpg" style="width: 800px" align=center> </p> <p align="center"> <a href="">Towards OCR-2.0 via a Unified End-to-end Model</a> </p>

Install

  1. 1.Our environment is cuda11.8+torch2.0.1
  2. 2.Clone this repository and navigate to the GOT folder
bash
git clone https://github.com/Ucas-HaoranWei/GOT-OCR2.0.git
cd 'the GOT folder'
  1. 1.Install Package
Shell
conda create -n got python=3.10 -y
conda activate got
pip install -e .
  1. 1.Install Flash-Attention
pip install ninja
pip install flash-attn --no-build-isolation

GOT Weights

Demo

  1. 1.plain texts OCR:
Shell
python3 GOT/demo/run_ocr_2.0.py  --model-name  /GOT_weights/  --image-file  /an/image/file.png  --type ocr
  1. 1.format texts OCR:
Shell
python3 GOT/demo/run_ocr_2.0.py  --model-name  /GOT_weights/  --image-file  /an/image/file.png  --type format
  1. 1.fine-grained OCR:
Shell
python3 GOT/demo/run_ocr_2.0.py  --model-name  /GOT_weights/  --image-file  /an/image/file.png  --type format/ocr --box [x1,y1,x2,y2]
Shell
python3 GOT/demo/run_ocr_2.0.py  --model-name  /GOT_weights/  --image-file  /an/image/file.png  --type format/ocr --color red/green/blue
  1. 1.multi-crop OCR:
Shell
python3 GOT/demo/run_ocr_2.0_crop.py  --model-name  /GOT_weights/ --image-file  /an/image/file.png
  1. 1.multi-page OCR (the image path contains multiple .png files):
Shell
python3 GOT/demo/run_ocr_2.0_crop.py  --model-name  /GOT_weights/ --image-file  /images/path/  --multi-page
  1. 1.render the formatted OCR results:
Shell
python3 GOT/demo/run_ocr_2.0.py  --model-name  /GOT_weights/  --image-file  /an/image/file.png  --type format --render

Note: The rendering results can be found in /results/demo.html. Please open the demo.html to see the results.

Train

  1. 1.This codebase only supports post-training (stage-2/stage-3) upon our GOT weights.
  2. 2.If you want train from stage-1 described in our paper, you need this repo.
Shell
deepspeed   /GOT-OCR-2.0-master/GOT/train/train_GOT.py \
 --deepspeed /GOT-OCR-2.0-master/zero_config/zero2.json    --model_name_or_path /GOT_weights/ \
 --use_im_start_end True   \
 --bf16 True   \
 --gradient_accumulation_steps 2    \
 --evaluation_strategy "no"   \
 --save_strategy "steps"  \
 --save_steps 200   \
 --save_total_limit 1   \
 --weight_decay 0.    \
 --warmup_ratio 0.001     \
 --lr_scheduler_type "cosine"    \
 --logging_steps 1    \
 --tf32 True     \
 --model_max_length 8192    \
 --gradient_checkpointing True   \
 --dataloader_num_workers 8    \
 --report_to none  \
 --per_device_train_batch_size 2    \
 --num_train_epochs 1  \
 --learning_rate 2e-5   \
 --datasets pdf-ocr+scence \
 --output_dir /your/output.path

Note:

  1. 1.Change the corresponding data information in constant.py.
  2. 2.Change line 37 in conversationdatasetqwen.py to your data_name.

Eval

  1. 1.We use the Fox and OneChart benchmarks, and other benchmarks can be found in the weights download link.
  2. 2.The eval codes can be found in GOT/eval.
  3. 3.You can use the evaluate_GOT.py to run the eval. If you have 8 GPUs， the --num-chunks can be set to 8.
Shell
python3 GOT/eval/evaluate_GOT.py --model-name /GOT_weights/ --gtfile_path xxxx.json --image_path  /image/path/ --out_path /data/eval_results/GOT_mathpix_test/ --num-chunks 8 --datatype OCR

Contact

If you are interested in this work or have questions about the code or the paper, please join our communication [Wechat]() group.

Acknowledgement

  • —Vary: the codebase we built upon!
  • —Qwen: the LLM base model of Vary, which is good at both English and Chinese!

Citation

bibtex
@article{wei2024general,
  title={General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model},
  author={Wei, Haoran and Liu, Chenglong and Chen, Jinyue and Wang, Jia and Kong, Lingyu and Xu, Yanming and Ge, Zheng and Zhao, Liang and Sun, Jianjian and Peng, Yuang and others},
  journal={arXiv preprint arXiv:2409.01704},
  year={2024}
}
@article{wei2023vary,
  title={Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models},
  author={Wei, Haoran and Kong, Lingyu and Chen, Jinyue and Zhao, Liang and Ge, Zheng and Yang, Jinrong and Sun, Jianjian and Han, Chunrui and Zhang, Xiangyu},
  journal={arXiv preprint arXiv:2312.06109},
  year={2023}
}