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Arabic text recognition
Joint Authors
Haraty, Ramzi A.
Ghaddar, Catherine
Source
The International Arab Journal of Information Technology
Issue
Vol. 1, Issue 2 (31 Jul. 2004), pp.156-163, 8 p.
Publisher
Publication Date
2004-07-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
The issue of handwritten character recognition is still a big challenge to the scientific community.
Several approaches to address this challenge have been attempted in the last years, mostly focusing on the English pre-printed or handwritten characters space.
Thus, the need to attempt a research related to Arabic handwritten text recognition.
Algorithms based on neural networks have proved to give better results than conventional methods when applied to problems where the decision rules of the classification problem are not clearly defined.
Two neural networks were built to classify already segmented characters of handwritten Arabic text.
The two neural networks correctly recognized 73 % of the characters.
However, one hurdle was encountered in the above scenario, which can be summarized as follows : there are a lot of handwritten characters that can be segmented and classified into two or more different classes depending on whether they are looked at separately, or in a word, or even in a sentence.
In other words, character classification, especially handwritten Arabic characters, depends largely on contextual information, not only on topographic features extracted from these characters.
American Psychological Association (APA)
Haraty, Ramzi A.& Ghaddar, Catherine. 2004. Arabic text recognition. The International Arab Journal of Information Technology،Vol. 1, no. 2, pp.156-163.
https://search.emarefa.net/detail/BIM-12479
Modern Language Association (MLA)
Haraty, Ramzi A.& Ghaddar, Catherine. Arabic text recognition. The International Arab Journal of Information Technology Vol. 1, no. 2 (Jul. 2004), pp.156-163.
https://search.emarefa.net/detail/BIM-12479
American Medical Association (AMA)
Haraty, Ramzi A.& Ghaddar, Catherine. Arabic text recognition. The International Arab Journal of Information Technology. 2004. Vol. 1, no. 2, pp.156-163.
https://search.emarefa.net/detail/BIM-12479
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 162-163
Record ID
BIM-12479