README.md

---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: image
    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: train
    num_bytes: 12570294352.965
    num_examples: 119495
  download_size: 12676311547
  dataset_size: 12570294352.965
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- image-to-image
language:
- en
size_categories:
- 100K<n<1M
tags:
- image-restoration
- text-recognition
- diffusion-models
- scene-text
---

# 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:
```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}
}