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Conference Paper: Recognition of handwritten Chinese characters by combining regularization, Fisher's discriminant and distorted sample generation

TitleRecognition of handwritten Chinese characters by combining regularization, Fisher's discriminant and distorted sample generation
Authors
KeywordsCovariance matrices
Dimension reduction techniques
Discriminant functions
Distortion model
Feature dimensions
Issue Date2009
PublisherIEEE, Computer Society.
Citation
Proceedings Of The International Conference On Document Analysis And Recognition, Icdar, 2009, p. 1026-1030 How to Cite?
AbstractThe problem of offline handwritten Chinese character recognition has been extensively studied by many researchers and very high recognition rates have been reported. In this paper, we propose to further boost the recognition rate by incorporating a distortion model that artificially generates a huge number of virtual training samples from existing ones. We achieve a record high recognition rate of 99.46% on the ETL-9B database. Traditionally, when the dimension of the feature vector is high and the number of training samples is not sufficient, the remedies are to (i) regularize the class covariance matrices in the discriminant functions, (ii) employ Fisher's dimension reduction technique to reduce the feature dimension, and (iii) generate a huge number of virtual training samples from existing ones. The second contribution of this paper is the investigation of the relative effectiveness of these three methods for boosting the recognition rate. © 2009 IEEE.
DescriptionProceedings of the 10th International Conference on Document Analysis and Recognition, 2009, p. 1026–1030
Persistent Identifierhttp://hdl.handle.net/10722/126103
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorLeung, KCen_HK
dc.contributor.authorLeung, CHen_HK
dc.date.accessioned2010-10-31T12:09:57Z-
dc.date.available2010-10-31T12:09:57Z-
dc.date.issued2009en_HK
dc.identifier.citationProceedings Of The International Conference On Document Analysis And Recognition, Icdar, 2009, p. 1026-1030en_HK
dc.identifier.issn1520-5363en_HK
dc.identifier.urihttp://hdl.handle.net/10722/126103-
dc.descriptionProceedings of the 10th International Conference on Document Analysis and Recognition, 2009, p. 1026–1030-
dc.description.abstractThe problem of offline handwritten Chinese character recognition has been extensively studied by many researchers and very high recognition rates have been reported. In this paper, we propose to further boost the recognition rate by incorporating a distortion model that artificially generates a huge number of virtual training samples from existing ones. We achieve a record high recognition rate of 99.46% on the ETL-9B database. Traditionally, when the dimension of the feature vector is high and the number of training samples is not sufficient, the remedies are to (i) regularize the class covariance matrices in the discriminant functions, (ii) employ Fisher's dimension reduction technique to reduce the feature dimension, and (iii) generate a huge number of virtual training samples from existing ones. The second contribution of this paper is the investigation of the relative effectiveness of these three methods for boosting the recognition rate. © 2009 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE, Computer Society.-
dc.relation.ispartofProceedings of the International Conference on Document Analysis and Recognition, ICDARen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsInternational Conference on Document Analysis and Recognition Proceedings. Copyright © IEEE, Computer Society.-
dc.rights©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectCovariance matrices-
dc.subjectDimension reduction techniques-
dc.subjectDiscriminant functions-
dc.subjectDistortion model-
dc.subjectFeature dimensions-
dc.titleRecognition of handwritten Chinese characters by combining regularization, Fisher's discriminant and distorted sample generationen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=15205363&volume=&spage=1026–1030&epage=&date=2009&atitle=Recognition+of+handwritten+Chinese+characters+by+combining+regularization,+Fisher’s+discriminant+and+distorted+sample+generation-
dc.identifier.emailLeung, KC:kcleung@eee.hku.hken_HK
dc.identifier.emailLeung, CH:chleung@eee.hku.hken_HK
dc.identifier.authorityLeung, KC=rp00147en_HK
dc.identifier.authorityLeung, CH=rp00146en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDAR.2009.48en_HK
dc.identifier.scopuseid_2-s2.0-70449725654en_HK
dc.identifier.hkuros181376en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70449725654&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage1026en_HK
dc.identifier.epage1030en_HK
dc.identifier.scopusauthoridLeung, KC=7401860663en_HK
dc.identifier.scopusauthoridLeung, CH=7402612415en_HK

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