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New paradigm for segmentation and recognition of handwritten numeral string

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dc.contributor.authorYoon S.-
dc.contributor.authorKim G.-
dc.contributor.authorChoi Y.-
dc.contributor.authorLee Y.-
dc.date.available2021-02-22T16:46:26Z-
dc.date.issued2002-08-
dc.identifier.issn1520-5363-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/16697-
dc.description.abstractThe string recognition is rather paradoxical problem because it requires the segmentation of the string into understandable units but proper segmentation needs a priori knowledge of the units and this implies recognition capability. To solve this dilemma, therefore both a priori knowledge of meaningful units and segmentation method have to be used together and they should dynamically interact with each other. In other words, results of segmentation are used as fundamental information to suppose what is most likely to be and then its a priori knowledge is used to help segmentation. This model makes explicit segmentation unnecessary because it does not speculate on possible break positions. It is also possible to recognize a digit even if it contains strokes that do not belong to to it. Using this paradigm for 100 handwritten numeral strings belonging to NIST database has resulted in 95% recognition. © 2001 IEEE.-
dc.format.extent5-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE Computer Society-
dc.titleNew paradigm for segmentation and recognition of handwritten numeral string-
dc.typeArticle-
dc.identifier.doi10.1109/ICDAR.2001.953784-
dc.identifier.scopusid2-s2.0-84929295356-
dc.identifier.bibliographicCitationProceedings of the International Conference on Document Analysis and Recognition, ICDAR, v.2001-January, pp 205 - 209-
dc.citation.titleProceedings of the International Conference on Document Analysis and Recognition, ICDAR-
dc.citation.volume2001-January-
dc.citation.startPage205-
dc.citation.endPage209-
dc.type.docTypeConference Paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusImage segmentation-
dc.subject.keywordPlusHandwritten numeral-
dc.subject.keywordPlusMost likely-
dc.subject.keywordPlusNIST database-
dc.subject.keywordPlusPriori knowledge-
dc.subject.keywordPlusSegmentation methods-
dc.subject.keywordPlusCharacter recognition-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/953784-
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