Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912220148924416 |
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| author | Saeed, Mehreen Chan, Adrian Mijar, Anupam Moukarzel, Joseph Habchi, Georges Younes, Carlos Elias, Amin Wong, Chau-Wai Khater, Akram |
| author_facet | Saeed, Mehreen Chan, Adrian Mijar, Anupam Moukarzel, Joseph Habchi, Georges Younes, Carlos Elias, Amin Wong, Chau-Wai Khater, Akram |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09630 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition Saeed, Mehreen Chan, Adrian Mijar, Anupam Moukarzel, Joseph Habchi, Georges Younes, Carlos Elias, Amin Wong, Chau-Wai Khater, Akram Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.09630 |