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An efficient form classification method using partial matching

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dc.contributor.authorByun Y.-
dc.contributor.authorYoon S.-
dc.contributor.authorChoi Y.-
dc.contributor.authorKim G.-
dc.contributor.authorLee Y.-
dc.date.available2021-02-22T16:46:29Z-
dc.date.issued2002-02-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/16701-
dc.description.abstractIn this paper, we are proposing an efficient method of classifying form that is applicable in real life. Our method will identify a small number of local regions by their distinctive images with respect to their layout structure and then by using the DP (Dynamic Programming) matching to match only these local regions. The disparity score in each local region is defined and measured to select the matching regions. Genetic Algorithm will also be applied to select the best regions of matching from the viewpoint of a performance. Our approach of searching and matching only a small number of structurally distinctive local regions would reduce the processing time and yield a high rate of classification. © Springer-Verlag Berlin Heidelberg 2001.-
dc.format.extent12-
dc.language영어-
dc.language.isoENG-
dc.publisherSpringer Verlag-
dc.titleAn efficient form classification method using partial matching-
dc.typeArticle-
dc.publisher.location독일-
dc.identifier.doi10.1007/3-540-45656-2_9-
dc.identifier.scopusid2-s2.0-84894543127-
dc.identifier.bibliographicCitationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.2256, pp 95 - 106-
dc.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.volume2256-
dc.citation.startPage95-
dc.citation.endPage106-
dc.type.docTypeConference Paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusArtificial intelligence-
dc.subject.keywordPlusDynamic programming-
dc.subject.keywordPlusGenetic algorithms-
dc.subject.keywordPlusClassification methods-
dc.subject.keywordPlusHigh rate-
dc.subject.keywordPlusLayout structure-
dc.subject.keywordPlusLocal region-
dc.subject.keywordPlusMatching-regions-
dc.subject.keywordPlusPartial matching-
dc.subject.keywordPlusProcessing time-
dc.subject.keywordPlusImage matching-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007%2F3-540-45656-2_9-
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공과대학 (소프트웨어학부(첨단))
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