An Investigation of the Modified Direction Feature for Cursive Character Recognition

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Title An Investigation of the Modified Direction Feature for Cursive Character Recognition
Author Blumenstein, Michael Myer; Liu, Xin Yu; Verma, Brijesh
Journal Name Pattern Recognition
Year Published 2007
Place of publication Oxford, UK
Publisher Elsevier Ltd.
Abstract This paper describes and analyses the performance of a novel feature extraction technique for the recognition of segmented/cursive characters that may be used in the context of a segmentation-based handwritten word recognition system. The modified direction feature (MDF) extraction technique builds upon the direction feature (DF) technique proposed previously that extracts direction information from the structure of character contours. This principal was extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image. In order to improve on the DF extraction technique, a number of modifications were undertaken. With a view to describe the character contour more effectively, a re-design of the direction number determination technique was performed. Also, an additional global feature was introduced to improve the recognition accuracy for those characters that were most frequently confused with patterns of similar appearance. MDF was tested using a neural network-based classifier and compared to the DF and transition feature (TF) extraction techniques. MDF outperformed both DF and TF techniques using a benchmark dataset and compared favourably with the top results in the literature. A recognition accuracy of above 89% is reported on characters from the CEDAR dataset.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.elsevier.com/wps/find/journaldescription.cws_home/328/description?navopenmenu=1
Alternative URI http://dx.doi.org/10.1016/j.patcog.2006.05.017
Copyright Statement Copyright 2007 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Volume 40
Issue Number 2
Page from 376
Page to 388
ISSN 0031-3203
Date Accessioned 2008-03-06
Date Available 2009-09-24T05:56:15Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Pattern Recognition
URI http://hdl.handle.net/10072/17976
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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