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International Journal of Advanced Research in Computer and Communication Engineering A monthly Peer-reviewed & Refereed journal
ISSN Online 2278-1021ISSN Print 2319-5940Since 2012
IJARCCE adheres to the suggestive parameters outlined by the University Grants Commission (UGC) for peer-reviewed journals, upholding high standards of research quality, ethical publishing, and academic excellence.
← Back to VOLUME 9, ISSUE 10, OCTOBER 2020

Online Arabic Handwriting Characters Recognition using Deep Learning

Khalid Mohammed Musa Yaagoup, Mohamed Elhafiz Mustafa

DOI: 10.17148/IJARCCE.2020.91014

Abstract: Recent research shows that in a wide variety of fields, such as computer vision, speech recognition and natural language processing, deep learning has produced noticeably promising results. Automatic recognition of handwriting is a significant component for many applications in different fields. It is a complex subject that has gained a great deal of attention in the past three decades. Research has focused on the recognition of Latin languages’ handwriting, fewer studies have been done for the Arabic language, a few problems still wait to be solved for Arabic handwritten characters. We presented a Convolutional Neural Network (CNN) model for the recognition of Arabic handwritten characters in this paper. The dataset is pre-processed before feeding it to the CNN model, it applied on database that contain 16800 of handwritten Arabic characters. The accuracy was raised to 96% as a test accuracy showing better results than other methods using the same database. Keywords: Deep Learning, Convolutional Neural Network, Handwritten Characters, Pre-Processed.

How to Cite:

[1] Khalid Mohammed Musa Yaagoup, Mohamed Elhafiz Mustafa, “Online Arabic Handwriting Characters Recognition using Deep Learning,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2020.91014