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A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters

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Title A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters
Author Blumenstein, Michael Myer; Verma, Brijesh Kumar; Basli, Hasan
Publication Title Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR '03)
Editor Bob Werner
Year Published 2003
Place of publication Los Alamitos, USA
Publisher IEEE Computer Society
Abstract High accuracy character recognition techniques can provide useful information for segmentation-based handwritten word recognition systems. This research describes neural network-based techniques for segmented character recognition that may be applied to the segmentation and recognition components of an off-line handwritten word recognition system. Two neural architectures along with two different feature extraction techniques were investigated. A novel technique for character feature extraction is discussed and compared with others in the literature. Recognition results above 80% are reported using characters automatically segmented from the CEDAR benchmark database as well as standard CEDAR alphanumerics.
Peer Reviewed Yes
Published Yes
Publisher URI http://ieeexplore.ieee.org/servlet/opac?punumber=8701
Alternative URI http://dx.doi.org/10.1109/ICDAR.2003.1227647
Copyright Statement Copyright 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ISBN 0-7695-1960-1
Conference name International Conference on Document Analysis and Recognition (ICDAR 2003)
Location Edinburgh, Scotland
Date From 2003-08-03
Date To 2003-08-06
URI http://hdl.handle.net/10072/1767
Date Accessioned 2004-03-31
Date Available 2009-09-24T05:54:47Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Engineering and Information Technology
Subject PRE2009-Pattern Recognition
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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