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On-line Handwritten Arabic Character Recognition using Artificial Neural Network

by Khaoula Addakiri, Mohamed Bahaj
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 55 - Number 13
Year of Publication: 2012
Authors: Khaoula Addakiri, Mohamed Bahaj
10.5120/8819-2819

Khaoula Addakiri, Mohamed Bahaj . On-line Handwritten Arabic Character Recognition using Artificial Neural Network. International Journal of Computer Applications. 55, 13 ( October 2012), 42-46. DOI=10.5120/8819-2819

@article{ 10.5120/8819-2819,
author = { Khaoula Addakiri, Mohamed Bahaj },
title = { On-line Handwritten Arabic Character Recognition using Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 13 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume55/number13/8819-2819/ },
doi = { 10.5120/8819-2819 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:57:10.658370+05:30
%A Khaoula Addakiri
%A Mohamed Bahaj
%T On-line Handwritten Arabic Character Recognition using Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 13
%P 42-46
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, an efficient approach for the recognition of on-line Arabic handwritten characters is presented. The method employed involves three phases: First, pre-processing in which the original image is transformed into a binary image . Second , training neural networks with feed-forward back propagation algorithm . Finally, the recognition of the character through the use of Neural Network techniques. The proposed approach is tested on 1400 different characters written by ten users. Each user wrote 28 Arabic characters five times in order to get different writing variations. Experiment results showed the effectiveness of our approach for recognizing handwritten Arabic characters.

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Index Terms

Computer Science
Information Sciences

Keywords

Pattern Recognition Neural Networks Arabic Handwritten

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