---
viewer: false
license: other
license_name: mixed-upstream
license_link: https://github.com/phamquiluan/jdeskew#dise-2021-dataset
task_categories:
- image-to-image
tags:
- document-image
- skew-estimation
- document-analysis
- dise-2021
size_categories:
- 10K<n<100K
---

# DISE 2021: Document Image Skew Estimation Dataset

DISE 2021 is a benchmark dataset for document image skew estimation, introduced in the ICIP 2022 paper
[Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation](https://arxiv.org/abs/2603.05942).

- **Paper:** https://arxiv.org/abs/2603.05942 (IEEE: https://ieeexplore.ieee.org/document/9897910)
- **Code:** https://github.com/phamquiluan/jdeskew
- **Zenodo mirror (DOI):** https://zenodo.org/records/12570649

## Contents

| File | Description |
| --- | --- |
| `dise2021_15.zip` | Skew angles within ±15° (10,764 PNG images) |
| `dise2021_45.zip` | Skew angles within ±45° |

The ground-truth skew angle of each image is embedded in its filename in square brackets,
e.g. `00771163[6.24].png` has a skew angle of **6.24°**.

```python
import re
from pathlib import Path

def get_angle_from_name(path):
    return float(re.search(r"\[(.+)\]", Path(path).stem).group(1))
```

## Usage with jdeskew

```python
pip install jdeskew
```

```python
from jdeskew.estimator import get_angle
from jdeskew.utility import rotate

angle = get_angle(image)
output_image = rotate(image, angle)
```

## License

This dataset is built upon three other datasets: **DISEC 2013**, **RVL-CDIP**, and **RDCL 2017**.
Please respect the licenses and terms of use of these upstream datasets when using DISE 2021.

## Citation

```bibtex
@inproceedings{pham2021dise,
  title={Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation},
  author={Luan Pham, Hao Hoang, Toan Mai, and Tuan Anh Tran},
  booktitle={2022 29th International Conference on Image Processing (ICIP)},
  year={2022},
  organization={IEEE}
}
```