Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition

Mehreen Saeed, Adrian Chan, Anupam Mijar, Joseph Moukarzel, Georges Habchi +4 more
6/13/2024
cs.CVcs.LG

No official code found. Best implementation: MehreenMehreen/muharaf (18 stars, last commit 2025-09-08). 1 more below.

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.

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Code Implementations(2)

MehreenMehreen/muharafCommunity100%

Muharaf dataset and related code

183May 22, 20241 years ago
MehreenMehreen/start_follow_read_arabicCommunity100%
43Python, Jupyter NotebookJun 6, 20241 years ago

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Cite this paper

@article{saeed2024muharaf,
  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},
  doi    = {10.48550/arXiv.2406.09630},
  url    = {http://arxiv.org/abs/2406.09630v2},
  journal = {NEURIPS 2024 2024}
}

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