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README.md

@@ -263,18 +263,29 @@ DeQA-Doc-Sharpness understands document context and can differentiate between in
 - Requires GPU with sufficient VRAM for efficient inference
 - Sharpness assessment is relative to training data distribution
 
+## Credits & Attribution
+
+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.
+
+**Original Repository**: [https://github.com/Junjie-Gao19/DeQA-Doc](https://github.com/Junjie-Gao19/DeQA-Doc)
+
+All credit for the research, training methodology, and model architecture goes to the original authors.
+
 ## Citation
 
+If you use this model in your research, please cite the original paper:
+
 ```bibtex
-@misc{deqa-doc-sharpness-2024,
-  title={DeQA-Doc-Sharpness: Document Image Sharpness Assessment},
-  author={mapo80},
-  year={2024},
-  publisher={HuggingFace},
-  url={https://huggingface.co/mapo80/DeQA-Doc-Sharpness}
+@inproceedings{deqadoc,
+  title={{DeQA-Doc}: Adapting {DeQA-Score} to Document Image Quality Assessment},
+  author={Gao, Junjie and Liu, Runze and Peng, Yingzhe and Yang, Shujian and Zhang, Jin and Yang, Kai and You, Zhiyuan},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop},
+  year={2025},
 }
 ```
 
+**ArXiv**: [https://arxiv.org/abs/2507.12796](https://arxiv.org/abs/2507.12796)
+
 ## License
 
 Apache 2.0