README.md

---
language:
- ar
license: cc-by-nc-sa-4.0
task_categories:
- image-to-text
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 1274826565.992
    num_examples: 22092
  - name: validation
    num_bytes: 60515423.616
    num_examples: 1069
  - name: test
    num_bytes: 71481336.008
    num_examples: 1334
  download_size: 1317284681
  dataset_size: 24495
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
tags:
- ocr
- htr
- historic
---

# Muharaf-public line images
This dataset contains 24,495 line images of Arabic handwriting and the corresponding text. The images are Manuscripts from the 19th to 21st century.
See the official code, paper, and zenodo archive below. Our work has been accepted to NeurIPS 2024.
[![Code](https://img.shields.io/badge/Code-black?style=flat-square&logo=github)](https://github.com/MehreenMehreen/muharaf) | [![Paper](https://img.shields.io/badge/Paper-black?style=flat-square&logo=arxiv)](https://arxiv.org/abs/2406.09630) | [![Dataset](https://img.shields.io/badge/Dataset-black?style=flat-square&logo=zenodo&logoColor=white&logoSize=auto)](https://zenodo.org/records/11492215)
:-------------------------:|:-------------------------:|:----

# How to use

```python
from datasets import load_dataset

ds = load_dataset("aamijar/muharaf-public")

# remember to index into the row first to avoid decoding all images at once (https://huggingface.co/docs/datasets/en/image_load#load-image-data)
print(ds["train"][0]["image"])

# instead of
```

<strike>

```python
print(ds["train"]["image"][0])
```

</strike>

# Citation

If you are using this dataset in your work, please cite our paper below.

```
@misc{saeed2024muharafmanuscriptshandwrittenarabic,
      title={Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition}, 
      author={Mehreen Saeed and Adrian Chan and Anupam Mijar and Joseph Moukarzel and Georges Habchi and Carlos Younes and Amin Elias and Chau-Wai Wong and Akram Khater},
      year={2024},
      eprint={2406.09630},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2406.09630}, 
}
```