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dc.contributor.authorThornton, Johnen_US
dc.contributor.authorFaichney, Jolonen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorNguyen, Vuen_US
dc.contributor.authorHine, Trevoren_US
dc.contributor.editorAnnie Vinter & Jean-Luc Velayen_US
dc.date.accessioned2017-05-03T12:54:36Z
dc.date.available2017-05-03T12:54:36Z
dc.date.issued2009en_US
dc.date.modified2012-09-02T22:46:36Z
dc.identifier.refurihttp://www.graphonomics.org/igs2009/en_US
dc.identifier.urihttp://hdl.handle.net/10072/31841
dc.description.abstractRecent 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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent79193 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherInternational Graphonomics Societyen_US
dc.publisher.placeDijon, Franceen_US
dc.publisher.urihttp://www.graphonomics.org/igs2009/en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencenameIGS 2009: 14th Biennial Conference of the International Graphonomics Societyen_US
dc.relation.ispartofconferencetitleAdvances in Graphonomics: Proceedings of IGS 2009en_US
dc.relation.ispartofdatefrom2009-09-13en_US
dc.relation.ispartofdateto2009-09-16en_US
dc.relation.ispartoflocationDijon, Franceen_US
dc.rights.retentionNen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080109en_US
dc.titleOffline Cursive Character Recognition: A state of the art comparisonen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 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.en_US
gro.date.issued2009
gro.hasfulltextFull Text


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