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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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--- |
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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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[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 NVIDIA GPUs. 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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megfile==3.1.2 |
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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('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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# 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 = image_file) |
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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{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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``` |
