| --- |
| pipeline_tag: image-text-to-text |
| language: |
| - multilingual |
| tags: |
| - got |
| - vision-language |
| - ocr2.0 |
| - custom_code |
| license: apache-2.0 |
| --- |
| <div align="center"> |
|
|
| # Neural Metrics · General OCR that does not flinch at formulas, tables or sheet music. |
|
|
| <img src="https://img.shields.io/badge/Neural%20Metrics-document%20extraction-4F46E5?style=for-the-badge" alt="Neural Metrics" /> |
| <img src="https://img.shields.io/badge/fork%20of-stepfun--ai%2FGOT--OCR2_0-2563EB?style=flat-square" alt="fork" /> |
|
|
| </div> |
|
|
| GOT-OCR2.0 is a unified end-to-end OCR model that handles scene text, dense document text, math, tables and charts, and can emit Markdown or LaTeX directly. |
|
|
| **We use it for:** dense text-heavy pages - documents with embedded math or chemical notation - producing Markdown that survives downstream parsing. |
|
|
| > ### Attribution |
| > This is an **unmodified fork** of [`stepfun-ai/GOT-OCR2_0`](https://huggingface.co/stepfun-ai/GOT-OCR2_0), created by the [Qwen team](https://huggingface.co/Qwen). |
| > All weights, files and behaviour are identical to upstream — we rehost it so our experiments stay |
| > reproducible and version-pinned. The original license and all credit remain with the Qwen team. |
| > If you want the canonical model, please use [the original](https://huggingface.co/stepfun-ai/GOT-OCR2_0). |
|
|
| --- |
|
|
| <details> |
| <summary><b>Original model card from stepfun-ai/GOT-OCR2_0</b> (click to expand)</summary> |
| |
| <h1>General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model |
| </h1> |
| |
| [🔋Online Demo](https://huggingface.co/spaces/ucaslcl/GOT_online) | [🌟GitHub](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/) | [📜Paper](https://arxiv.org/abs/2409.01704)</a> |
| |
| |
| [Haoran Wei*](https://scholar.google.com/citations?user=J4naK0MAAAAJ&hl=en), Chenglong Liu*, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, [Zheng Ge](https://joker316701882.github.io/), Liang Zhao, [Jianjian Sun](https://scholar.google.com/citations?user=MVZrGkYAAAAJ&hl=en), [Yuang Peng](https://scholar.google.com.hk/citations?user=J0ko04IAAAAJ&hl=zh-CN&oi=ao), Chunrui Han, [Xiangyu Zhang](https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=en) |
| |
| |
| |
|  |
| |
| |
| |
| ## Usage |
| Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10： |
| ``` |
| torch==2.0.1 |
| torchvision==0.15.2 |
| transformers==4.37.2 |
| tiktoken==0.6.0 |
| verovio==4.3.1 |
| accelerate==0.28.0 |
| ``` |
| |
| |
| ```python |
| from transformers import AutoModel, AutoTokenizer |
| |
| tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True) |
| model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id) |
| model = model.eval().cuda() |
|
|
|
|
| # input your test image |
| image_file = 'xxx.jpg' |
| |
| # plain texts OCR |
| res = model.chat(tokenizer, image_file, ocr_type='ocr') |
| |
| # format texts OCR: |
| # res = model.chat(tokenizer, image_file, ocr_type='format') |
| |
| # fine-grained OCR: |
| # res = model.chat(tokenizer, image_file, ocr_type='ocr', ocr_box='') |
| # res = model.chat(tokenizer, image_file, ocr_type='format', ocr_box='') |
| # res = model.chat(tokenizer, image_file, ocr_type='ocr', ocr_color='') |
| # res = model.chat(tokenizer, image_file, ocr_type='format', ocr_color='') |
| |
| # multi-crop OCR: |
| # res = model.chat_crop(tokenizer, image_file, ocr_type='ocr') |
| # res = model.chat_crop(tokenizer, image_file, ocr_type='format') |
| |
| # render the formatted OCR results: |
| # res = model.chat(tokenizer, image_file, ocr_type='format', render=True, save_render_file = './demo.html') |
| |
| print(res) |
| |
| |
| ``` |
| More details about 'ocr_type', 'ocr_box', 'ocr_color', and 'render' can be found at our GitHub. |
| Our training codes are available at our [GitHub](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/). |
|
|
|
|
|
|
| ## More Multimodal Projects |
|
|
| 👏 Welcome to explore more multimodal projects of our team: |
|
|
| [Vary](https://github.com/Ucas-HaoranWei/Vary) | [Fox](https://github.com/ucaslcl/Fox) | [OneChart](https://github.com/LingyvKong/OneChart) |
|
|
| ## Citation |
|
|
| If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！ |
|
|
| ```bib |
| @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{liu2024focus, |
| title={Focus Anywhere for Fine-grained Multi-page Document Understanding}, |
| author={Liu, Chenglong and Wei, Haoran and Chen, Jinyue and Kong, Lingyu and Ge, Zheng and Zhu, Zining and Zhao, Liang and Sun, Jianjian and Han, Chunrui and Zhang, Xiangyu}, |
| journal={arXiv preprint arXiv:2405.14295}, |
| 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} |
| } |
| ``` |
|
|
| </details> |
|
|
