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dataset_info:
features:
- name: id
dtype: string
- name: hq_img
dtype: image
- name: lq_img_lv1
dtype: image
- name: lq_img_lv2
dtype: image
- name: lq_img_lv3
dtype: image
- name: text
sequence: string
- name: bbox
sequence:
array2_d:
shape:
- 2
- 2
dtype: int32
- name: poly
sequence:
array2_d:
shape:
- 16
- 2
dtype: int32
splits:
- name: test
num_bytes: 119459110.0
num_examples: 1000
download_size: 118582963
dataset_size: 119459110.0
configs:
- config_name: default
data_files:
- split: test
path: data/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](https://huggingface.co/datasets/Min-Jaewon/SA-Text)
- 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:
```bibtex
@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}
}
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