Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/248
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLu, Z-
dc.creatorChi, ZG-
dc.creatorSiu, WC-
dc.date.accessioned2014-12-11T08:27:13Z-
dc.date.available2014-12-11T08:27:13Z-
dc.identifier.issn0162-8828-
dc.identifier.urihttp://hdl.handle.net/10397/248-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2002 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.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectConnected handwritten digit recognitionen_US
dc.subjectPixel-to-boundary distance mapen_US
dc.subjectB-spline fittingen_US
dc.subjectDigit templatesen_US
dc.subjectTemplate optimizationen_US
dc.subjectNearest neighbor classifieren_US
dc.subjectMultilayer perceptronen_US
dc.subjectEvolutionary algorithmen_US
dc.titleExtraction and optimization of B-spline PBD templates for recognition of connected handwritten digit stringsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage132-
dc.identifier.epage139-
dc.identifier.volume24-
dc.identifier.issue1-
dc.identifier.doi10.1109/34.982890-
dcterms.abstractRecognition of connected handwritten digit strings is a challenging task due mainly to two problems: poor character segmentation and unreliable isolated character recognition. In this paper, we first present a rational B-spline representation of digit templates based on Pixel-to-boundary Distance (PBD) maps. We then present a neural network approach to extract B-spline PBD templates and an evolutionary algorithm to optimize these templates. In total, 1,000 templates (100 templates for each of 10 classes) were extracted from and optimized on 10,426 training samples from the NIST Special Database 3. By using these templates, a nearest neighbor classifier can successfully reject 90.7 percent of nondigit patterns while achieving a 96.4 percent correct classification of isolated test digits. When our classifier is applied to the recognition of 4,958 connected handwritten digit strings (4,555 2-digit, 355 3-digit, and 48 4-digit strings) from the NIST Special Database 3 with a dynamic programming approach, it has a correct classification rate of 82.4 percent with a rejection rate of as low as 0.85 percent. Our classifier compares favorably in terms of correct classification rate and robustness with other classifiers that are tested.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on pattern analysis and machine intelligence, Jan. 2002, v. 24, no. 1, p. 132-139-
dcterms.isPartOfIEEE transactions on pattern analysis and machine intelligence-
dcterms.issued2002-01-
dc.identifier.isiWOS:000172960300010-
dc.identifier.scopus2-s2.0-0036229125-
dc.identifier.eissn1939-3539-
dc.identifier.rosgroupidr07800-
dc.description.ros2001-2002 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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