An efficient form classification method using partial matching
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Byun Y. | - |
dc.contributor.author | Yoon S. | - |
dc.contributor.author | Choi Y. | - |
dc.contributor.author | Kim G. | - |
dc.contributor.author | Lee Y. | - |
dc.date.available | 2021-02-22T16:46:29Z | - |
dc.date.issued | 2002-02 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/16701 | - |
dc.description.abstract | In 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.extent | 12 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Springer Verlag | - |
dc.title | An efficient form classification method using partial matching | - |
dc.type | Article | - |
dc.publisher.location | 독일 | - |
dc.identifier.doi | 10.1007/3-540-45656-2_9 | - |
dc.identifier.scopusid | 2-s2.0-84894543127 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.2256, pp 95 - 106 | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 2256 | - |
dc.citation.startPage | 95 | - |
dc.citation.endPage | 106 | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Artificial intelligence | - |
dc.subject.keywordPlus | Dynamic programming | - |
dc.subject.keywordPlus | Genetic algorithms | - |
dc.subject.keywordPlus | Classification methods | - |
dc.subject.keywordPlus | High rate | - |
dc.subject.keywordPlus | Layout structure | - |
dc.subject.keywordPlus | Local region | - |
dc.subject.keywordPlus | Matching-regions | - |
dc.subject.keywordPlus | Partial matching | - |
dc.subject.keywordPlus | Processing time | - |
dc.subject.keywordPlus | Image matching | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007%2F3-540-45656-2_9 | - |
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