Cursive Character Segmentation Using Neural Network Techniques

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Title Cursive Character Segmentation Using Neural Network Techniques
Author Blumenstein, Michael Myer
Book Title Machine Learning in Document Analysis and Recognition
Editor Simone Marinai and Hiromichi Fujisawa
Year Published 2008
Place of publication Berlin Heidelberg
Publisher Springer-Verlag
Abstract The segmentation of cursive and mixed scripts persists to be a difficult problem in the area of handwriting recognition. This research details advances for segmenting characters in off-line cursive script. Specifically, a heuristic algorithm and a neural network-based technique, which uses a structural feature vector representation, are proposed and combined for identifying incorrect segmentation points. Following the location of appropriate anchorage points, a character extraction technique, using segmentation paths, is employed to complete the segmentation process. Results are presented for neural-based heuristic segmentation, segmentation point validation, character recognition, segmentation path detection and overall segmentation accuracy.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.springerlink.com/
Alternative URI http://dx.doi.org/10.1007/978-3-540-76280-5_10
Edition First
Chapter Number 10
Page from 259
Page to 275
ISBN 9783540762799
Date Accessioned 2009-03-12
Date Available 2011-05-13T06:56:01Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Pattern Recognition and Data Mining
URI http://hdl.handle.net/10072/23529
Publication Type Book Chapters
Publication Type Code b1

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