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. 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.
• For the test sets of SA-Text, check SA-Text-test

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}
}