Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0031-3203(01)00228-X
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dc.titleRobust vision-based features and classification schemes for off-line handwritten digit recognition
dc.contributor.authorTeow, L.-N.
dc.contributor.authorLoe, K.-F.
dc.date.accessioned2013-07-23T09:25:15Z
dc.date.available2013-07-23T09:25:15Z
dc.date.issued2002
dc.identifier.citationTeow, L.-N., Loe, K.-F. (2002). Robust vision-based features and classification schemes for off-line handwritten digit recognition. Pattern Recognition 35 (11) : 2355-2364. ScholarBank@NUS Repository. https://doi.org/10.1016/S0031-3203(01)00228-X
dc.identifier.issn00313203
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/43111
dc.description.abstractWe use well-established results in biological vision to construct a model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear discriminant system on these features, our model is relatively simple yet outperforms other models on the same data set. In particular, the best result is obtained by applying triowise linear support vector machines with soft voting on vision-based features extracted from deslanted images. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0031-3203(01)00228-X
dc.sourceScopus
dc.subjectBiological vision
dc.subjectFeature extraction
dc.subjectHandwritten digit recognition
dc.subjectLinear discrimination
dc.subjectMulticlass classification
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.departmentINSTITUTE OF SYSTEMS SCIENCE
dc.description.doi10.1016/S0031-3203(01)00228-X
dc.description.sourcetitlePattern Recognition
dc.description.volume35
dc.description.issue11
dc.description.page2355-2364
dc.description.codenPTNRA
dc.identifier.isiut000177636300003
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