Handwritten Devnagari Character Recognition using Soft Computing | Basics,concepts and some soft computing techniques

Sandhya Arora (u. a.)

ISBN 10: 3659278416 / ISBN 13: 9783659278419

Published by LAP LAMBERT Academic Publishing, 2012

About this Item

Handwritten Devnagari Character Recognition using Soft Computing | Basics,concepts and some soft computing techniques | Sandhya Arora (u. a.) | Taschenbuch | 156 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659278419 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 106171039

Bibliographic Details

• Title: Handwritten Devnagari Character Recognition using Soft Computing | Basics,concepts and some soft computing techniques
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3659278416
• ISBN 13: 9783659278419
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 250 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

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.