Feature extraction and selection algorithm for chain code representation of handwritten character

Isolated characters, especially Latin characters, usually contain many branches on their characters’ nodes that causes difficulties to decide which direction would a traverse continues. Furthermore, a revisit to previous visited nodes is often required in order to visit all the nodes in one continuo...

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Main Author:	Nasien, Dewi
Format:	Thesis
Language:	English
Published:	2012
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/32091/5/DewiNasienPFSKSM2012.pdf
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id	my-utm-ep.32091
record_format	uketd_dc
spelling	my-utm-ep.320912018-04-30T13:09:40Z Feature extraction and selection algorithm for chain code representation of handwritten character 2012-02 Nasien, Dewi QA75 Electronic computers. Computer science Isolated characters, especially Latin characters, usually contain many branches on their characters’ nodes that causes difficulties to decide which direction would a traverse continues. Furthermore, a revisit to previous visited nodes is often required in order to visit all the nodes in one continuous route. In this thesis, some techniques to solve problems for Handwritten Character Recognition (HCR) involving isolated characters are proposed. HCR consists of three stages which are pre-processing, feature extraction and classification. In the pre-processing, thinning algorithm was applied to remove the redundancies of pixel in character binary image. In the feature extraction, Freeman Chain Code (FCC) was used as data representation that uses 8-neighbourhood directions labelled as 1 to 8. However, the FCC representation is dependent on the route length and branches of the characters’ node. The larger the number of branches, which is common for isolated characters, the longer the time required for the extraction. Here, a FCC extraction based on Heuristic Randomized-based algorithm was proposed to reduce the route length and computational time. Based on the experiment, it was demonstrated that the proposed FCC extraction is superior in terms of producing the shortest route length with minimum computational time, compared to Enumeration-based algorithm, Genetic Algorithm and Ant Colony Optimization. In this thesis, features vector extracted using the FCC extraction was used as input to the classification. There were 69 features used, 64 features were from the chain codes and 5 features were from original image. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were chosen as classifier in the classification of image characters. The performance of ANN is better than SVM in terms of accuracy. The accuracy of ANN on sample data from the National Institute of Standards and Technology database reached more than 96% for all upper-case and lower-case, more than 98% for all upper-case, lower-case and characters, and more than 90% for digits only. 2012-02 Thesis http://eprints.utm.my/id/eprint/32091/ http://eprints.utm.my/id/eprint/32091/5/DewiNasienPFSKSM2012.pdf application/pdf en public phd doctoral 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	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Nasien, Dewi Feature extraction and selection algorithm for chain code representation of handwritten character
description	Isolated characters, especially Latin characters, usually contain many branches on their characters’ nodes that causes difficulties to decide which direction would a traverse continues. Furthermore, a revisit to previous visited nodes is often required in order to visit all the nodes in one continuous route. In this thesis, some techniques to solve problems for Handwritten Character Recognition (HCR) involving isolated characters are proposed. HCR consists of three stages which are pre-processing, feature extraction and classification. In the pre-processing, thinning algorithm was applied to remove the redundancies of pixel in character binary image. In the feature extraction, Freeman Chain Code (FCC) was used as data representation that uses 8-neighbourhood directions labelled as 1 to 8. However, the FCC representation is dependent on the route length and branches of the characters’ node. The larger the number of branches, which is common for isolated characters, the longer the time required for the extraction. Here, a FCC extraction based on Heuristic Randomized-based algorithm was proposed to reduce the route length and computational time. Based on the experiment, it was demonstrated that the proposed FCC extraction is superior in terms of producing the shortest route length with minimum computational time, compared to Enumeration-based algorithm, Genetic Algorithm and Ant Colony Optimization. In this thesis, features vector extracted using the FCC extraction was used as input to the classification. There were 69 features used, 64 features were from the chain codes and 5 features were from original image. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were chosen as classifier in the classification of image characters. The performance of ANN is better than SVM in terms of accuracy. The accuracy of ANN on sample data from the National Institute of Standards and Technology database reached more than 96% for all upper-case and lower-case, more than 98% for all upper-case, lower-case and characters, and more than 90% for digits only.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Nasien, Dewi
author_facet	Nasien, Dewi
author_sort	Nasien, Dewi
title	Feature extraction and selection algorithm for chain code representation of handwritten character
title_short	Feature extraction and selection algorithm for chain code representation of handwritten character
title_full	Feature extraction and selection algorithm for chain code representation of handwritten character
title_fullStr	Feature extraction and selection algorithm for chain code representation of handwritten character
title_full_unstemmed	Feature extraction and selection algorithm for chain code representation of handwritten character
title_sort	feature extraction and selection algorithm for chain code representation of handwritten character
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2012
url	http://eprints.utm.my/id/eprint/32091/5/DewiNasienPFSKSM2012.pdf
_version_	1747815919184248832

Feature extraction and selection algorithm for chain code representation of handwritten character

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