Network-based approach to Korean handwriting analysis

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dc.contributor.authorSin, BKko
dc.contributor.authorKim, JinHyungko
dc.date.accessioned2013-03-03T04:47:24Z-
dc.date.available2013-03-03T04:47:24Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued1998-03-
dc.identifier.citationINTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.12, no.2, pp.233 - 249-
dc.identifier.issn0218-0014-
dc.identifier.urihttp://hdl.handle.net/10203/77289-
dc.description.abstractIt is well known that the stochastic approach using the HMM and dynamic programming-based search is particularly suited to the analysis of time series signals including on-line handwriting. The starting point of this research is a network of HMMs which models the whole set of characters. Then it is followed by the assertion that the HMM for the on-line script can be applied to not only on-line character recognition but also to the handwriting synthesis and even pen-trajectory recovery in off-line character images. The solutions to these problems are based on the single network of HMMs and the single principle of DP-based state-observation alignment. Given an observation sequence, the search for the best path in the network corresponds to the recognition. Given a character model, the search for the best observation sequence corresponds to the handwriting generation. The proposed framework has been shown to work nicely through a set of tests on Korean characters.-
dc.languageEnglish-
dc.publisherWORLD SCIENTIFIC PUBL CO PTE LTD-
dc.subjectRECOGNITION-
dc.titleNetwork-based approach to Korean handwriting analysis-
dc.typeArticle-
dc.identifier.wosid000073849600006-
dc.identifier.scopusid2-s2.0-0242719504-
dc.type.rimsART-
dc.citation.volume12-
dc.citation.issue2-
dc.citation.beginningpage233-
dc.citation.endingpage249-
dc.citation.publicationnameINTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE-
dc.contributor.localauthorKim, JinHyung-
dc.contributor.nonIdAuthorSin, BK-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorcharacter recognition-
dc.subject.keywordAuthorhandwriting generation-
dc.subject.keywordAuthorpen trajectory recovery-
dc.subject.keywordAuthorhidden Markov model-
dc.subject.keywordAuthorViterbi algorithm-
dc.subject.keywordAuthorKorean character-
dc.subject.keywordPlusRECOGNITION-
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