A self-organizing hierarchical classifier for multi-lingual large-set oriental character recognition
DC Field | Value | Language |
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dc.contributor.author | Park, HS | - |
dc.contributor.author | Song, HH | - |
dc.contributor.author | Lee, SW | - |
dc.date.accessioned | 2021-09-09T12:40:07Z | - |
dc.date.available | 2021-09-09T12:40:07Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 1998-03 | - |
dc.identifier.issn | 0218-0014 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/124430 | - |
dc.description.abstract | In this paper, we propose a practical scheme for multi-lingual, multi-font and multi-size large-set Oriental character recognition using a self-organizing hierarchical neural network classifier. In order to absorb the variation of the character shapes in multi-font and multi-size characters, a modified nonlinear shape normalization method based on dot density was introduced, and also to represent the different topological structures of multilingual characters effectively, a hierarchical feature extraction method was adopted. For coarse classification, a tree classifier and SOFM/LVQ based classifier which is composed of an adaptive SOFM coarse-classifier and an LVQ4 language-classifier were considered. For fine classification, a classifier based on LVQ4 learning algorithm has been developed. The experimental results revealed that the proposed scheme has the highest recognition rate of 98.27% for testing data with 7,320 kinds of multi-lingual classes and the time performance of more than 40 characters per second on 486DX-2 66MHz PC. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WORLD SCIENTIFIC PUBL CO PTE LTD | - |
dc.subject | NETWORK | - |
dc.title | A self-organizing hierarchical classifier for multi-lingual large-set oriental character recognition | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, SW | - |
dc.identifier.doi | 10.1142/S0218001498000130 | - |
dc.identifier.scopusid | 2-s2.0-11744254978 | - |
dc.identifier.wosid | 000073849600003 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.12, no.2, pp.191 - 208 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | - |
dc.citation.title | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | - |
dc.citation.volume | 12 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 191 | - |
dc.citation.endPage | 208 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | NETWORK | - |
dc.subject.keywordAuthor | multi-lingual character recognition | - |
dc.subject.keywordAuthor | Oriental character recognition | - |
dc.subject.keywordAuthor | SOFM | - |
dc.subject.keywordAuthor | LVQ4 | - |
dc.subject.keywordAuthor | language classifier | - |
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