Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition
Abstract
We present the Manuscripts of Handwritten Arabic (Muharaf) dataset, which is a machine learning dataset consisting of more than 1,600 historic handwritten page images transcribed by experts in archival Arabic. Each document image is accompanied by spatial polygonal coordinates of its text lines as well as basic page elements. This dataset was compiled to advance the state of the art in handwritten text recognition (HTR), not only for Arabic manuscripts but also for cursive text in general. The Muharaf dataset includes diverse handwriting styles and a wide range of document types, including personal letters, diaries, notes, poems, church records, and legal correspondences. In this paper, we describe the data acquisition pipeline, notable dataset features, and statistics. We also provide a preliminary baseline result achieved by training convolutional neural networks using this data.
Cite
Text
Saeed et al. "Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition." Neural Information Processing Systems, 2024. doi:10.52202/079017-1865Markdown
[Saeed et al. "Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/saeed2024neurips-muharaf/) doi:10.52202/079017-1865BibTeX
@inproceedings{saeed2024neurips-muharaf,
title = {{Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition}},
author = {Saeed, Mehreen and Chan, Adrian and Mijar, Anupam and Moukarzel, Joseph and Habchi, Georges and Younes, Carlos and Elias, Amin and Wong, Chau-Wai and Khater, Akram},
booktitle = {Neural Information Processing Systems},
year = {2024},
doi = {10.52202/079017-1865},
url = {https://mlanthology.org/neurips/2024/saeed2024neurips-muharaf/}
}