General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model

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

Release

• [2024/10/24] The previous four wechat groups are full, so we created a fifth group.
• [2024/10/11] Too many friends want to join the wechat group, so we created a fourth group.
• [2024/10/2] onnx and mnn versions of GOT-OCR2.0.
• [2024/9/29]🔥🔥🔥 The community has implemented the first version of llama_cpp_inference.
• [2024/9/24]🔥🔥🔥 Support ms-swift quick Fine-tune for your own data.
• [2024/9/23]🔥🔥🔥 We release the official Modelscope demo. Thanks very much for Modelscope providing the GPU resource.
• [2024/9/14]🔥🔥🔥 We release the official demo. Thanks very much for Huggingface providing the GPU resource.
• [2024/9/13]🔥🔥🔥 We release the Huggingface deployment.
• [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!

Community contributions

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

vllm reference ~ contributor: @Jay

onnx and mnn supports ~ contributor: @BaofengZan

llama_cpp inference ~ contributor: @1694439208

Colab of GOT ~ contributor: @Zizhe Wang

CPU version of GOT ~ contributor: @ElvisClaros

Online demo ~ contributor: @Joseph Pollack

Dokcer & client demo ~ contributor: @QIN2DIM

GUI of GOT ~ contributor: @XJF2332

Contents

• Install
• GOT Weights
• Demo
• Train
• Fine-tune
• Eval

---

Towards OCR-2.0 via a Unified End-to-end Model

---

Install

0. Our environment is cuda11.8+torch2.0.1
1. Clone this repository and navigate to the GOT folder

git clone https://github.com/Ucas-HaoranWei/GOT-OCR2.0.git
cd 'the GOT folder'

2. Install Package

conda create -n got python=3.10 -y
conda activate got
pip install -e .

3. Install Flash-Attention

pip install ninja
pip install flash-attn --no-build-isolation

GOT Weights

• Huggingface
• Google Drive
• BaiduYun code: OCR2

Demo

1. plain texts OCR:

python3 GOT/demo/run_ocr_2.0.py  --model-name  /GOT_weights/  --image-file  /an/image/file.png  --type ocr

2. format texts OCR:

python3 GOT/demo/run_ocr_2.0.py  --model-name  /GOT_weights/  --image-file  /an/image/file.png  --type format

3. fine-grained OCR:

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]

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

4. multi-crop OCR:

python3 GOT/demo/run_ocr_2.0_crop.py  --model-name  /GOT_weights/ --image-file  /an/image/file.png 

5. multi-page OCR (the image path contains multiple .png files):

python3 GOT/demo/run_ocr_2.0_crop.py  --model-name  /GOT_weights/ --image-file  /images/path/  --multi-page

6. render the formatted OCR results:

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

0. Train sample can be found here. Note that the '<image>' in the 'conversations'-'human'-'value' is necessary!
1. This codebase only supports post-training (stage-2/stage-3) upon our GOT weights.
2. If you want to train from stage-1 described in our paper, you need this repo.

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

Fine-tune

Quick Fine-tune with ms-swift:

git clone https://github.com/modelscope/ms-swift.git
cd ms-swift
pip install -e .[llm]

# default：sft LLM & projector, freeze vision encoder
CUDA_VISIBLE_DEVICES=0 swift sft\
--model_type got-ocr2 \
--model_id_or_path stepfun-ai/GOT-OCR2_0 \
--sft_type lora \
--dataset latex-ocr-print#5000

# Deepspeed ZeRO2
NPROC_PER_NODE=4 \
CUDA_VISIBLE_DEVICES=0,1,2,3 swift sft \
--model_type got-ocr2 \
--model_id_or_path stepfun-ai/GOT-OCR2_0 \
--sft_type lora \
--dataset latex-ocr-print#5000 \
--deepspeed default-zero2

With your data:

--dataset train.jsonl
--val_dataset val.jsonl (optional)

Data format:

{"query": "<image>55555", "response": "66666", "images": ["image_path"]}
{"query": "<image><image>eeeee", "response": "fffff", "history": [], "images": ["image_path1", "image_path2"]}
{"query": "EEEEE", "response": "FFFFF", "history": [["query1", "response1"], ["query2", "response2"]]}

More details can be seen in ms-swift.

Eval

1. We use the Fox and OneChart benchmarks, and other benchmarks can be found in the weights download link.
2. The eval codes can be found in GOT/eval.
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.

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.

Note: All four wechat groups are full, please join group 5.

Don't hesitate to contact me by email, [email protected], if you have any questions.

Acknowledgement

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

Citation

@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}
}