README.md

---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: hq_img
    dtype: image
  - name: lq_img
    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: 55089874.0
    num_examples: 847
  download_size: 54622145
  dataset_size: 55089874.0
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
language:
- en
size_categories:
- 10M<n<100M
task_categories:
- image-to-image
tags:
- image-restoration
- diffusion-models
- text-recognition
---

# 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](https://github.com/csjcai/RealSR) and [DrealSR](https://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution) 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](https://huggingface.co/datasets/Min-Jaewon/SA-Text) for training dataset.
- Please refer to our [dataset pipeline](https://github.com/paulcho98/text_restoration_dataset).

## 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}
}
```