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Recognition techniques for online Arabic handwriting recognition systems

Abuzaraida, Mustafa Ali and Zeki, Akram M. and Zeki, Ahmed M. (2012) Recognition techniques for online Arabic handwriting recognition systems. In: International Conference on Advanced Computer Science Applications and Technologies (ACSAT2012), 26-28 November 2012, Kuala Lumpur, Malaysia.

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Abstract

Online recognition of Arabic handwritten text has been an on-going research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. However, different techniques have been used to build several online handwritten recognition systems for Arabic text, such as Neural Networks, Hidden Markov Model, Template Matching and others. Most of the researches on online text recognition have divided the recognition system into these three main phases which are preprocessing phase, feature extraction phase and recognition phase which considers as the most important phase and the heart of the whole system. This paper presents and compares techniques that have been used to recognize the Arabic handwriting scripts in online recognition systems. Those techniques attempt to recognize Arabic handwritten words, characters, digits or strokes. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 6153/30803 --- ISBN: 978-076954959-0/13
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Information and Communication Technology > Department of Information System
Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User: Akram M Zeki
Date Deposited: 22 Aug 2013 12:56
Last Modified: 08 Dec 2014 15:36
URI: http://irep.iium.edu.my/id/eprint/30803

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