Update README.md

README.md

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   - split: train
     path: data/train-*
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+
+# SA-Text
+
+**Text-Aware Image Restoration with Diffusion Models** (arXiv:2506.09993)  
+Large-scale training dataset for the **Text-Aware Image Restoration (TAIR)** task.
+
+- 📄 Paper: https://arxiv.org/abs/2506.09993  
+- 🌐 Project Page: https://cvlab-kaist.github.io/TAIR/  
+- 💻 GitHub: https://github.com/cvlab-kaist/TAIR  
+- 🛠 Dataset Pipeline: https://github.com/paulcho98/text_restoration_dataset
+
+## Dataset Description
+
+**SA-Text** is constructed from SA-1B dataset using our official [dataset pipeline](https://github.com/paulcho98/text_restoration_dataset). It contains **100K** high-resolution scene images paired with polygon-level text annotations.
+This dataset is tailored for TAIR task, which aims to restore both visual quality and text fidelity in degraded images.
+
+## Notes
+
+- Each image includes one or more **text instances** with transcriptions and polygon-level labels.
+- Designed for training **TeReDiff**, a multi-task diffusion model introduced in our paper.
+- For real-world evaluation, check [Real-Text](https://huggingface.co/datasets/Min-Jaewon/Real-Text).
+
+## Citation
+Please cite the following paper if you use this dataset:
+```
+{
+  @article{min2024textaware,
+  title={Text-Aware Image Restoration with Diffusion Models},
+  author={Min, Jaewon and Kim, Jin Hyeon and Cho, Paul Hyunbin and Lee, Jaeeun and Park, Jihye and Park, Minkyu and Kim, Sangpil and Park, Hyunhee and Kim, Seungryong},
+  journal={arXiv preprint arXiv:2506.09993},
+  year={2025}
+}
+```