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A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT
Shaheed Banazir Bhutto University, Sheringal, Pakistan.
Computer Science Department, GGPGC No.1 Abbottabad, Pakistan.
Mindgarage, University of Kaiserslautern, Germany.
Al Khwarizmi Institute of Computer Science, UET Lahore, Pakistan.
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2020 (English)In: The International Arab Journal of Information Technology, ISSN 1683-3198, Vol. 17, no 3, p. 299-305Article in journal (Refereed) Published
Abstract [en]

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Place, publisher, year, edition, pages
Zarqa University, Jordan , 2020. Vol. 17, no 3, p. 299-305
Keywords [en]
Handwritten Arabic text recognition, deep learning, data augmentation
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-78876DOI: 10.34028/iajit/17/3/3ISI: 000529820700003OAI: oai:DiVA.org:ltu-78876DiVA, id: diva2:1430307
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Validerad;2020;Nivå 2;2020-05-14 (alebob)

Available from: 2020-05-14 Created: 2020-05-14 Last updated: 2020-05-14Bibliographically approved

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Liwicki, Marcus

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