A hybrid approach of hidden Markov model and fuzzy logic for isolated handwritten characters recognition

Research in off-line handwriting recognition has been prevalent for many decades. After many years of intense research, it still remains an open problem. The challenging nature of handwritten characters and words recognition has attracted the attention of researchers from industry and academic circl...

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Main Author:	Suliman, Azizah
Format:	Thesis
Language:	English
Published:	2011
Subjects:	Markov processes Fuzzy logic Perceptrons
Online Access:	http://psasir.upm.edu.my/id/eprint/27376/1/FSKTM%202011%2015R.pdf
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id	my-upm-ir.27376
record_format	uketd_dc
spelling	my-upm-ir.273762014-02-27T01:03:06Z A hybrid approach of hidden Markov model and fuzzy logic for isolated handwritten characters recognition 2011-08 Suliman, Azizah Research in off-line handwriting recognition has been prevalent for many decades. After many years of intense research, it still remains an open problem. The challenging nature of handwritten characters and words recognition has attracted the attention of researchers from industry and academic circles. The commercial sector has shown significant interest in handwriting recognition research due to the large number of applications that exist. In recent years, techniques for recognizing handwriting have become more sophisticated in dealing with real-world situation and to increase recognition rates. This thesis reviews all aspects of handwriting recognition research, from the recognition of handwritten numerals to handwritten words. The different methods employed by researchers are mentioned and the approaches adopted for the research are elaborated. The focus of this thesis would be the recognition of isolated handwritten characters, concentrating on the slightly more challenging group, lowercase English alphabets. The main aim of this research work is to present a hybrid approach of a syntactical method with a statistical method in a manner that will require less training data but still yield reasonable recognition rate and high reliability rate. The system will be designed with the use of Hidden Markov Model (HMM) as a linguistic variable quantifier for a Fuzzy rule based classifier. This hybrid method, as far as according to the result of the literature search is concerned, is among the first in the area of handwriting recognition. The main advantage of this approach is a less training intensive method that does not rely on data abundance to achieve a good recognition result. The whole system that integrates the two approaches is tested against a standard database. A favorable outcome of the recognition results, has proven the approach is comparable to many other approaches as in the literature. Markov processes Fuzzy logic Perceptrons 2011-08 Thesis http://psasir.upm.edu.my/id/eprint/27376/ http://psasir.upm.edu.my/id/eprint/27376/1/FSKTM%202011%2015R.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Markov processes Fuzzy logic Perceptrons Faculty of Computer Science and Information Technology
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Markov processes Fuzzy logic Perceptrons
spellingShingle	Markov processes Fuzzy logic Perceptrons Suliman, Azizah A hybrid approach of hidden Markov model and fuzzy logic for isolated handwritten characters recognition
description	Research in off-line handwriting recognition has been prevalent for many decades. After many years of intense research, it still remains an open problem. The challenging nature of handwritten characters and words recognition has attracted the attention of researchers from industry and academic circles. The commercial sector has shown significant interest in handwriting recognition research due to the large number of applications that exist. In recent years, techniques for recognizing handwriting have become more sophisticated in dealing with real-world situation and to increase recognition rates. This thesis reviews all aspects of handwriting recognition research, from the recognition of handwritten numerals to handwritten words. The different methods employed by researchers are mentioned and the approaches adopted for the research are elaborated. The focus of this thesis would be the recognition of isolated handwritten characters, concentrating on the slightly more challenging group, lowercase English alphabets. The main aim of this research work is to present a hybrid approach of a syntactical method with a statistical method in a manner that will require less training data but still yield reasonable recognition rate and high reliability rate. The system will be designed with the use of Hidden Markov Model (HMM) as a linguistic variable quantifier for a Fuzzy rule based classifier. This hybrid method, as far as according to the result of the literature search is concerned, is among the first in the area of handwriting recognition. The main advantage of this approach is a less training intensive method that does not rely on data abundance to achieve a good recognition result. The whole system that integrates the two approaches is tested against a standard database. A favorable outcome of the recognition results, has proven the approach is comparable to many other approaches as in the literature.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Suliman, Azizah
author_facet	Suliman, Azizah
author_sort	Suliman, Azizah
title	A hybrid approach of hidden Markov model and fuzzy logic for isolated handwritten characters recognition
title_short	A hybrid approach of hidden Markov model and fuzzy logic for isolated handwritten characters recognition
title_full	A hybrid approach of hidden Markov model and fuzzy logic for isolated handwritten characters recognition
title_fullStr	A hybrid approach of hidden Markov model and fuzzy logic for isolated handwritten characters recognition
title_full_unstemmed	A hybrid approach of hidden Markov model and fuzzy logic for isolated handwritten characters recognition
title_sort	hybrid approach of hidden markov model and fuzzy logic for isolated handwritten characters recognition
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2011
url	http://psasir.upm.edu.my/id/eprint/27376/1/FSKTM%202011%2015R.pdf
_version_	1747811588740481024

A hybrid approach of hidden Markov model and fuzzy logic for isolated handwritten characters recognition

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