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Handwritten Devnagari Character Recognition using Soft Computing: Basics,concepts and some soft computing techniques - Softcover
Inhaltsangabe
Handwriting recognition is a difficult problem, because of the great amount of variations in human handwriting. When observed in isolation also, characters are often ambiguous. We have implemented some soft computing techniques using structural and statistical feature sets for constrained handwritten isolated Devnagari characters and numerals. Some preprocessing steps are applied before extracting statistical and structural information of character image. As conventional histogram based method does not work for handwritten Devnagari characters, differential distance based technique is designed to find shirorekha and spine. Multilayer perceptron, support vector machines and edit distance classifiers are used for classification. Three MLP combination techniques namely: max, min and weighted majority scheme is applied. Two approaches for two stage classification is discussed in detail to improve the accuracy. As there are many similar shaped characters, character set is grouped in two sets-- certainty and confused character set. Relative difference measure is used for grouping of character sets. Each set is classified using different classifier.
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Reseña del editor
Handwriting recognition is a difficult problem, because of the great amount of variations in human handwriting. When observed in isolation also, characters are often ambiguous. We have implemented some soft computing techniques using structural and statistical feature sets for constrained handwritten isolated Devnagari characters and numerals. Some preprocessing steps are applied before extracting statistical and structural information of character image. As conventional histogram based method does not work for handwritten Devnagari characters, differential distance based technique is designed to find shirorekha and spine. Multilayer perceptron, support vector machines and edit distance classifiers are used for classification. Three MLP combination techniques namely: max, min and weighted majority scheme is applied. Two approaches for two stage classification is discussed in detail to improve the accuracy. As there are many similar shaped characters, character set is grouped in two sets-- certainty and confused character set. Relative difference measure is used for grouping of character sets. Each set is classified using different classifier.
Biografía del autor
SANDHYA ARORA is PhD., M.Tech. and B.E.(Computer Engineering), India. She is currently working as Assistant Professor in India,having teaching experience of 15 years. She has presented 5 papers in national and 4 papers in international conferences and 21 published papers. Her research interests include pattern recognition, image processing.
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Handwritten Devnagari Character Recognition using Soft Computing
Verlag:
LAP LAMBERT Academic Publishing Nov 2012, 2012
ISBN 10: 3659278416
ISBN 13: 9783659278419
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Taschenbuch. Zustand: Neu. Neuware -Handwriting recognition is a difficult problem, because of the great amount of variations in human handwriting. When observed in isolation also, characters are often ambiguous. We have implemented some soft computing techniques using structural and statistical feature sets for constrained handwritten isolated Devnagari characters and numerals. Some preprocessing steps are applied before extracting statistical and structural information of character image. As conventional histogram based method does not work for handwritten Devnagari characters, differential distance based technique is designed to find shirorekha and spine. Multilayer perceptron, support vector machines and edit distance classifiers are used for classification. Three MLP combination techniques namely: max, min and weighted majority scheme is applied. Two approaches for two stage classification is discussed in detail to improve the accuracy. As there are many similar shaped characters, character set is grouped in two sets-- certainty and confused character set. Relative difference measure is used for grouping of character sets. Each set is classified using different classifier.Books on Demand GmbH, Überseering 33, 22297 Hamburg 156 pp. Englisch. Artikel-Nr. 9783659278419
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