Real-Text

Text-Aware Image Restoration with Diffusion Models (arXiv:2506.09993)
Real-world evaluation dataset for the 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

Real-Text is an evaluation dataset constructed from RealSR and DrealSR using the same pipeline as SA-Text. It reflects real-world degradation and distortion, making it suitable for robust benchmarking.

Notes

• This dataset is designed for testing oour model, TeReDiff, under realistic settings.
• Check SA-Text for training dataset.
• Check SA-Text-test for test datasets.
• Please refer to our dataset pipeline.

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}
}