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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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-
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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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- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6653eee7a2d7a882a805ab95/QCEFY-M_YG3Bp5fn1GQ8X.jpeg)
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  ## Usage
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  Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10:
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  ```python
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  from transformers import AutoModel, AutoTokenizer
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- tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
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- 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)
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  model = model.eval().cuda()
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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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-
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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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- ```
 
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  license: apache-2.0
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  ---
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  ## Usage
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  Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10:
 
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  ```python
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  from transformers import AutoModel, AutoTokenizer
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+ tokenizer = AutoTokenizer.from_pretrained('Cherryblade29/ocr', trust_remote_code=True)
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+ model = AutoModel.from_pretrained('Cherryblade29/ocr', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
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  model = model.eval().cuda()
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  ```
 
 
 
 
 
 
 
 
 
 
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