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---
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pipeline_tag: image-text-to-text
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library_name: transformers
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language:
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- multilingual
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tags:
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- got
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- vision-language
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- ocr2.0
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- custom_code
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license: apache-2.0
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---
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<h1>General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
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</h1>
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[🔋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>
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[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)
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## Usage
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Inference using Huggingface transformers on CPU. Requirements tested on python 3.10：
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```
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torch==2.0.1
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torchvision==0.15.2
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transformers==4.37.2
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tiktoken==0.6.0
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verovio==4.3.1
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accelerate==0.28.0
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```
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```python
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from transformers import AutoModel, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True)
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model = AutoModel.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True, low_cpu_mem_usage=True, use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
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model = model.eval()
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# input your test image
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image_file = 'xxx.jpg'
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# plain texts OCR
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res = model.chat(tokenizer, image_file, ocr_type='ocr')
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# format texts OCR:
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# res = model.chat(tokenizer, image_file, ocr_type='format')
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# fine-grained OCR:
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# res = model.chat(tokenizer, image_file, ocr_type='ocr', ocr_box='')
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# res = model.chat(tokenizer, image_file, ocr_type='format', ocr_box='')
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# res = model.chat(tokenizer, image_file, ocr_type='ocr', ocr_color='')
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# res = model.chat(tokenizer, image_file, ocr_type='format', ocr_color='')
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# multi-crop OCR:
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# res = model.chat_crop(tokenizer, image_file, ocr_type='ocr')
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# res = model.chat_crop(tokenizer, image_file, ocr_type='format')
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# render the formatted OCR results:
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# res = model.chat(tokenizer, image_file, ocr_type='format', render=True, save_render_file = './demo.html')
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print(res)
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```
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More details about 'ocr_type', 'ocr_box', 'ocr_color', and 'render' can be found at our GitHub.
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Our training codes are available at our [GitHub](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/).
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## More Multimodal Projects
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👏 Welcome to explore more multimodal projects of our team:
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[Vary](https://github.com/Ucas-HaoranWei/Vary) | [Fox](https://github.com/ucaslcl/Fox) | [OneChart](https://github.com/LingyvKong/OneChart)
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## Citation
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If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！
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```bib
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@article{wei2024general,
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title={General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model},
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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},
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journal={arXiv preprint arXiv:2409.01704},
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year={2024}
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}
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@article{liu2024focus,
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title={Focus Anywhere for Fine-grained Multi-page Document Understanding},
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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},
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journal={arXiv preprint arXiv:2405.14295},
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year={2024}
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}
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@article{wei2023vary,
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title={Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models},
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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},
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journal={arXiv preprint arXiv:2312.06109},
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year={2023}
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}
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``` |
