Features selection techniques for off-line handwritten isolated Arabic characters

Offline Handwritten isolated Arabic characters’ software has become a highly demand application to the machine reading of bank and post offices. In the past few years, several approaches have been used in the development of handwritten recognition applications. However, the recognition of handwritte...

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Main Author:	Naji, Aseel Shakir
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/33168/1/AseelShakirNajiMFSKSM2013.pdf
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id	my-utm-ep.33168
record_format	uketd_dc
spelling	my-utm-ep.331682017-09-14T04:24:00Z Features selection techniques for off-line handwritten isolated Arabic characters 2013-01 Naji, Aseel Shakir TA Engineering (General). Civil engineering (General) Offline Handwritten isolated Arabic characters’ software has become a highly demand application to the machine reading of bank and post offices. In the past few years, several approaches have been used in the development of handwritten recognition applications. However, the recognition of handwritten Arabic characters is a difficult task because of the similar appearance of some different characters.In this study, the moments: contour sequence, geometric and Zernike moments are employed on handwritten characters to select the efficient features. The classification and recognition process are applied using Neural Network technique and the results are analyzed to determine the necessity of thinning and unthinning processes. The database consists of 6885 images of characters: 75% of training and 25% of testing in the network. Matlab tool is implemented to perform the classification and recognition processes. Results obtained have shown that thinning process should be excluded as it deteriorates the recognition accuracy. The experiments resulted 97.58% in Contour Sequence moments with unthinning for classification and 95.25% for recognition process. Thus, Contour Sequence moments with unthinning process exhibited the highest recognition rate as compared to Geometric moments and Zernike moments. 2013-01 Thesis http://eprints.utm.my/id/eprint/33168/ http://eprints.utm.my/id/eprint/33168/1/AseelShakirNajiMFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70725?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Naji, Aseel Shakir Features selection techniques for off-line handwritten isolated Arabic characters
description	Offline Handwritten isolated Arabic characters’ software has become a highly demand application to the machine reading of bank and post offices. In the past few years, several approaches have been used in the development of handwritten recognition applications. However, the recognition of handwritten Arabic characters is a difficult task because of the similar appearance of some different characters.In this study, the moments: contour sequence, geometric and Zernike moments are employed on handwritten characters to select the efficient features. The classification and recognition process are applied using Neural Network technique and the results are analyzed to determine the necessity of thinning and unthinning processes. The database consists of 6885 images of characters: 75% of training and 25% of testing in the network. Matlab tool is implemented to perform the classification and recognition processes. Results obtained have shown that thinning process should be excluded as it deteriorates the recognition accuracy. The experiments resulted 97.58% in Contour Sequence moments with unthinning for classification and 95.25% for recognition process. Thus, Contour Sequence moments with unthinning process exhibited the highest recognition rate as compared to Geometric moments and Zernike moments.
format	Thesis
qualification_level	Master's degree
author	Naji, Aseel Shakir
author_facet	Naji, Aseel Shakir
author_sort	Naji, Aseel Shakir
title	Features selection techniques for off-line handwritten isolated Arabic characters
title_short	Features selection techniques for off-line handwritten isolated Arabic characters
title_full	Features selection techniques for off-line handwritten isolated Arabic characters
title_fullStr	Features selection techniques for off-line handwritten isolated Arabic characters
title_full_unstemmed	Features selection techniques for off-line handwritten isolated Arabic characters
title_sort	features selection techniques for off-line handwritten isolated arabic characters
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2013
url	http://eprints.utm.my/id/eprint/33168/1/AseelShakirNajiMFSKSM2013.pdf
_version_	1747816095971016704

Features selection techniques for off-line handwritten isolated Arabic characters

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