Min-Jaewon/SA-Text-test

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

The test set is organized into three degradation levels (lv1–lv3) with overlapping severity ranges, and stochastic degradation kernels make the ordering non-strict.

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 the training set of SA-Text, check SA-Text
• For real-world evaluation, check 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}
}