Network-based approach to online cursive script recognition

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DC FieldValueLanguage
dc.contributor.authorSin, BKko
dc.contributor.authorHa, JYko
dc.contributor.authorOh, SCko
dc.contributor.authorKim, JinHyungko
dc.date.accessioned2009-11-05T01:27:36Z-
dc.date.available2009-11-05T01:27:36Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued1999-04-
dc.identifier.citationIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, v.29, no.2, pp.321 - 328-
dc.identifier.issn1083-4419-
dc.identifier.urihttp://hdl.handle.net/10203/12126-
dc.description.abstractThe idea of combining the network of HMM's and the dynamic programming-based search is highly relevant to online handwriting recognition. The word model of HMM network can be systematically constructed by concatenating letter and ligature HMM's while sharing common ones. Character recognition in such a network can be defined as the task of best aligning a given input sequence to the best path in the network. One distinguishing feature of the approach is that letter segmentation is obtained simultaneously with recognition but no extra-computation is required.-
dc.languageEnglish-
dc.language.isoen_USen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleNetwork-based approach to online cursive script recognition-
dc.typeArticle-
dc.identifier.wosid000079319900020-
dc.identifier.scopusid2-s2.0-0033115629-
dc.type.rimsART-
dc.citation.volume29-
dc.citation.issue2-
dc.citation.beginningpage321-
dc.citation.endingpage328-
dc.citation.publicationnameIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorKim, JinHyung-
dc.contributor.nonIdAuthorSin, BK-
dc.contributor.nonIdAuthorHa, JY-
dc.contributor.nonIdAuthorOh, SC-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorcursive script-
dc.subject.keywordAuthorhidden Markov model-
dc.subject.keywordAuthorligature-
dc.subject.keywordAuthornetwork search-
dc.subject.keywordAuthoronline character recognition-
dc.subject.keywordAuthorsegmentation-
dc.subject.keywordAuthorViterbi algorithm-
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