Show simple item record

dc.contributor.authorThornton, John
dc.contributor.authorFaichney, Jolon
dc.contributor.authorBlumenstein, Michael
dc.contributor.authorNguyen, Vu
dc.contributor.authorHine, Trevor
dc.contributor.editorAnnie Vinter & Jean-Luc Velay
dc.date.accessioned2017-05-03T12:54:36Z
dc.date.available2017-05-03T12:54:36Z
dc.date.issued2009
dc.date.modified2012-09-02T22:46:36Z
dc.identifier.refurihttp://www.graphonomics.org/igs2009/
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent79193 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherInternational Graphonomics Society
dc.publisher.placeDijon, France
dc.publisher.urihttp://www.graphonomics.org/igs2009/
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameIGS 2009: 14th Biennial Conference of the International Graphonomics Society
dc.relation.ispartofconferencetitleAdvances in Graphonomics: Proceedings of IGS 2009
dc.relation.ispartofdatefrom2009-09-13
dc.relation.ispartofdateto2009-09-16
dc.relation.ispartoflocationDijon, France
dc.rights.retentionN
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleOffline Cursive Character Recognition: A state of the art comparison
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.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.
gro.date.issued2009
gro.hasfulltextFull Text
gro.griffith.authorThornton, John R.
gro.griffith.authorFaichney, Jolon B.
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorHine, Trevor J.
gro.griffith.authorNguyen, Vu


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record