Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition

Mehreen Saeed, Adrian Chan, Anupam Mijar, joseph Moukarzel, Gerges Habchi, Carlos Younes, amin elias, Chau-Wai Wong, Akram Khater

Keywords: machine learning dataset, handwritten text recognition (HTR), optical character recognition (OCR), Arabic dataset, Ruq`ah script

TL;DR: The paper describes the Muharaf Dataset for handwritten text recognition (HTR), its characteristics, collection pipeline and baseline results of training a machine learning system with this dataset.

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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Submission Number: 580