Datasets:
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---
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}
}
``` |