xmlui.dri2xhtml.structural.

A Modified Direction Feature for Cursive Character Recognition

File Size Format
27927.pdf 354Kb Adobe PDF View
Title A Modified Direction Feature for Cursive Character Recognition
Author Blumenstein, Michael Myer; Liu, Xin Yu; Verma, B.
Publication Title Proceedings of the 2004 IEEE International Joint Conference on Neural Networks
Editor Tamas Roska
Year Published 2004
Place of publication USA
Publisher Institute of Electrical and Electronics Engineers [CPS] (IEEE)
Abstract This paper describes a neural network-based technique for cursive character recognition applicable to segmentation-based word recognition systems. The proposed research builds on a novel feature extraction technique that extracts direction information from the structure of character contours. This principal is extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image. The proposed technique is compared with the standard direction feature extraction technique, providing promising results using segmented characters from the CEDAR benchmark database.
Peer Reviewed Yes
Published Yes
Publisher URI http://ieeexplore.ieee.org/servlet/opac?punumber=9486
Alternative URI http://dx.doi.org/10.1109/IJCNN.2004.1381140
Copyright Statement Copyright 2004 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 0-7803-8360-5
Conference name 2004 International Joint Conference on Neural Networks (IJCNN)
Location Budapest, Hungary
Date From 2004-07-25
Date To 2004-07-29
URI http://hdl.handle.net/10072/2115
Date Accessioned 2005-03-30
Date Available 2009-09-24T05:54:04Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Engineering and Information Technology
Subject PRE2009-Neural Networks, Genetic Alogrithms and Fuzzy Logic
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

Brief Record

Griffith University copyright notice