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@@ -207,18 +207,29 @@ pip install torch transformers accelerate pillow sentencepiece protobuf
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  - Black & white documents may receive lower scores (use Overall model instead)
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  - Requires GPU with sufficient VRAM for efficient inference
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  ## Citation
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  ```bibtex
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- @misc{deqa-doc-color-2024,
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- title={DeQA-Doc-Color: Document Image Color Quality Assessment},
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- author={mapo80},
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- year={2024},
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- publisher={HuggingFace},
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- url={https://huggingface.co/mapo80/DeQA-Doc-Color}
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  }
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  ```
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  ## License
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  Apache 2.0
 
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  - Black & white documents may receive lower scores (use Overall model instead)
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  - Requires GPU with sufficient VRAM for efficient inference
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+ ## Credits & Attribution
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+
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+ This model is based on the **DeQA-Doc** project by Junjie Gao et al., which won the **Championship** in the VQualA 2025 DIQA (Document Image Quality Assessment) Challenge.
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+
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+ **Original Repository**: [https://github.com/Junjie-Gao19/DeQA-Doc](https://github.com/Junjie-Gao19/DeQA-Doc)
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+ All credit for the research, training methodology, and model architecture goes to the original authors.
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  ## Citation
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+ If you use this model in your research, please cite the original paper:
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+
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  ```bibtex
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+ @inproceedings{deqadoc,
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+ title={{DeQA-Doc}: Adapting {DeQA-Score} to Document Image Quality Assessment},
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+ author={Gao, Junjie and Liu, Runze and Peng, Yingzhe and Yang, Shujian and Zhang, Jin and Yang, Kai and You, Zhiyuan},
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+ booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop},
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+ year={2025},
 
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  }
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  ```
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+ **ArXiv**: [https://arxiv.org/abs/2507.12796](https://arxiv.org/abs/2507.12796)
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+
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  ## License
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  Apache 2.0