Off-line cursive handwriting segmentation of isolated words based on contour analysis

Cursive handwriting is writing style acquainted by nicely link between adjacent characters. Nowadays, cursive handwriting recognition is widely used in various applications including bank check processing and automatic address reading. The objective of such handwriting recognition is to realize a ma...

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Main Author:	Kurniawan, Fajri
Format:	Thesis
Published:	2010
Subjects:	Unspecified
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id	my-utm-ep.33702
record_format	uketd_dc
spelling	my-utm-ep.337022017-08-14T03:53:02Z Off-line cursive handwriting segmentation of isolated words based on contour analysis 2010 Kurniawan, Fajri Unspecified Cursive handwriting is writing style acquainted by nicely link between adjacent characters. Nowadays, cursive handwriting recognition is widely used in various applications including bank check processing and automatic address reading. The objective of such handwriting recognition is to realize a machine that is able to understand the meaning of cursive handwriting. In the literature, cursive handwriting can be recognized based on character or whole-word recognition. Character-based recognition has advantage over whole-word recognition because the vocabulary can be dynamically defined and adjusted without the need of word training. However, the challenge in cursive handwriting segmentation is how to decompose sequence characters which need to be isolated. In addition, segmentation becomes more problematic in touching and overlapping cases. The aim of this research is to design off-line cursive handwriting segmentation using heuristic approach based on contour analysis along with segmentation point validation and touching character segmentation. Prospective segmentation points are determined by considering minimum number of cutting strokes and vertical projection. Heuristic segmentation determines whether the segmentation points are correct or incorrect. Thus, artificial neural network is adopted to validate each segmentation point. In this regard, the neural network is trained with two segmentation point classes i.e. correct and incorrect segmentation points. Afterward, segmentation based on self-organizing map (SOM) is applied to enhance segmentation accuracy. The experimental results show that neural validation and SOM segmentation have improved the segmentation accuracy. This is proven by the average segmentation error rate achieved which is 2.92% for the proposed segmentation method and 3.76% for the existing method, Enhanced Heuristic Segmentation (EHS). 2010 Thesis http://eprints.utm.my/id/eprint/33702/ 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
topic	Unspecified
spellingShingle	Unspecified Kurniawan, Fajri Off-line cursive handwriting segmentation of isolated words based on contour analysis
description	Cursive handwriting is writing style acquainted by nicely link between adjacent characters. Nowadays, cursive handwriting recognition is widely used in various applications including bank check processing and automatic address reading. The objective of such handwriting recognition is to realize a machine that is able to understand the meaning of cursive handwriting. In the literature, cursive handwriting can be recognized based on character or whole-word recognition. Character-based recognition has advantage over whole-word recognition because the vocabulary can be dynamically defined and adjusted without the need of word training. However, the challenge in cursive handwriting segmentation is how to decompose sequence characters which need to be isolated. In addition, segmentation becomes more problematic in touching and overlapping cases. The aim of this research is to design off-line cursive handwriting segmentation using heuristic approach based on contour analysis along with segmentation point validation and touching character segmentation. Prospective segmentation points are determined by considering minimum number of cutting strokes and vertical projection. Heuristic segmentation determines whether the segmentation points are correct or incorrect. Thus, artificial neural network is adopted to validate each segmentation point. In this regard, the neural network is trained with two segmentation point classes i.e. correct and incorrect segmentation points. Afterward, segmentation based on self-organizing map (SOM) is applied to enhance segmentation accuracy. The experimental results show that neural validation and SOM segmentation have improved the segmentation accuracy. This is proven by the average segmentation error rate achieved which is 2.92% for the proposed segmentation method and 3.76% for the existing method, Enhanced Heuristic Segmentation (EHS).
format	Thesis
qualification_level	Master's degree
author	Kurniawan, Fajri
author_facet	Kurniawan, Fajri
author_sort	Kurniawan, Fajri
title	Off-line cursive handwriting segmentation of isolated words based on contour analysis
title_short	Off-line cursive handwriting segmentation of isolated words based on contour analysis
title_full	Off-line cursive handwriting segmentation of isolated words based on contour analysis
title_fullStr	Off-line cursive handwriting segmentation of isolated words based on contour analysis
title_full_unstemmed	Off-line cursive handwriting segmentation of isolated words based on contour analysis
title_sort	off-line cursive handwriting segmentation of isolated words based on contour analysis
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2010
_version_	1747816164254285824

Off-line cursive handwriting segmentation of isolated words based on contour analysis

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