Offline handwritten Arabic writer identification using negative selection algorithm

In pattern recognition; writer identification is one of the research are as that attract the researchers’ interest in the conduct of their studies. Writer’s identification of identity and its determination ability is not the only important thing to the writer, but the accuracy of this determination...

Full description

Saved in:

Bibliographic Details
Main Author:	A. Suliman, Hager
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/50815/25/HagerASulimanMFC2014.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.50815
record_format	uketd_dc
spelling	my-utm-ep.508152020-07-12T07:25:16Z Offline handwritten Arabic writer identification using negative selection algorithm 2014-07 A. Suliman, Hager QA75 Electronic computers. Computer science In pattern recognition; writer identification is one of the research are as that attract the researchers’ interest in the conduct of their studies. Writer’s identification of identity and its determination ability is not the only important thing to the writer, but the accuracy of this determination is considered as a big challenge. This study evaluates the accuracy of Arabic handwriting identification performance using the Bio-Inspired classifier. The study shows that the accuracy of the identification performance could be greatly improved with the Bio-Inspired classifier. The framework of the writer identification consists of three main phases: pre-processing phase, feature extraction phase, and classification phase. This research adopts IFN/ENIT Arabic Database which is constructed by Ecole National ed’Ingénieur de Tunis (ENIT) in Tunisia and Institute of Communications Technology in Germany (IFN). The images are enhanced by applying the threshold and conversion of the gray scale level images into black and white. Geometric Moment Function is used to extract the features from the images. Finally, the Bio-Inspired classifier is applied in this research with the use of Negative Selection Algorithm to classify and identify the writer. The obtained results show a promising ability of NSA in Writer identification. Other researchers could apply the NSA on handwriting languages that uses the same Arabic letters with different semantic such as Urdu as well as Farsi. 2014-07 Thesis http://eprints.utm.my/id/eprint/50815/ http://eprints.utm.my/id/eprint/50815/25/HagerASulimanMFC2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85443 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science A. Suliman, Hager Offline handwritten Arabic writer identification using negative selection algorithm
description	In pattern recognition; writer identification is one of the research are as that attract the researchers’ interest in the conduct of their studies. Writer’s identification of identity and its determination ability is not the only important thing to the writer, but the accuracy of this determination is considered as a big challenge. This study evaluates the accuracy of Arabic handwriting identification performance using the Bio-Inspired classifier. The study shows that the accuracy of the identification performance could be greatly improved with the Bio-Inspired classifier. The framework of the writer identification consists of three main phases: pre-processing phase, feature extraction phase, and classification phase. This research adopts IFN/ENIT Arabic Database which is constructed by Ecole National ed’Ingénieur de Tunis (ENIT) in Tunisia and Institute of Communications Technology in Germany (IFN). The images are enhanced by applying the threshold and conversion of the gray scale level images into black and white. Geometric Moment Function is used to extract the features from the images. Finally, the Bio-Inspired classifier is applied in this research with the use of Negative Selection Algorithm to classify and identify the writer. The obtained results show a promising ability of NSA in Writer identification. Other researchers could apply the NSA on handwriting languages that uses the same Arabic letters with different semantic such as Urdu as well as Farsi.
format	Thesis
qualification_level	Master's degree
author	A. Suliman, Hager
author_facet	A. Suliman, Hager
author_sort	A. Suliman, Hager
title	Offline handwritten Arabic writer identification using negative selection algorithm
title_short	Offline handwritten Arabic writer identification using negative selection algorithm
title_full	Offline handwritten Arabic writer identification using negative selection algorithm
title_fullStr	Offline handwritten Arabic writer identification using negative selection algorithm
title_full_unstemmed	Offline handwritten Arabic writer identification using negative selection algorithm
title_sort	offline handwritten arabic writer identification using negative selection algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/50815/25/HagerASulimanMFC2014.pdf
_version_	1747817540535451648

Offline handwritten Arabic writer identification using negative selection algorithm

Similar Items