Offline Cursive Character Recognition: A state of the art comparison

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Title Offline Cursive Character Recognition: A state of the art comparison
Author Thornton, John Richard; Faichney, Jolon Bryce; Blumenstein, Michael Myer; Nguyen, Vu; Hine, Trevor John
Publication Title Advances in Graphonomics: Proceedings of IGS 2009
Editor Annie Vinter & Jean-Luc Velay
Year Published 2009
Place of publication Dijon, France
Publisher International Graphonomics Society
Abstract Recent research has demonstrated the superiority of SVM-based approaches for offline cursive character recognition. In particular, Camastra’s 2007 study showed SVM to be better than alternative LVQ and MLP approaches on the large C-Cube data set. Subsequent work has applied hierarchical vector quantization (HVQ) with temporal pooling to the same data set, improving on LVQ and MLP but still not reaching SVM recognition rates. In the current paper, we revisit Camastra’s SVM study in order to explore the effects of using an alternative modified direction feature (MDF) vector representation, and to compare the performance of a RBF-based approach against both SVM and HVQ. Our results show that SVMs still have the better performance, but that much depends on the feature sets employed. Surprisingly, the use of more sophisticated MDF feature vectors produced the poorest results on this data set despite their success on signature verification problems.
Peer Reviewed Yes
Published Yes
Publisher URI
Copyright Statement Copyright 2009 International Graphonomics Society. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
Conference name IGS 2009: 14th Biennial Conference of the International Graphonomics Society
Location Dijon, France
Date From 2009-09-13
Date To 2009-09-16
Date Accessioned 2009-07-08
Language en_US
Research Centre Behavioural Basis of Health; Menzies Health Institute Qld; Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Pattern Recognition and Data Mining
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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