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A 2-D HMM method for offline handwritten character recognition

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dc.contributor.authorPark, HS-
dc.contributor.authorSin, BK-
dc.contributor.authorMoon, J-
dc.contributor.authorLee, SW-
dc.date.accessioned2021-09-09T12:34:42Z-
dc.date.available2021-09-09T12:34:42Z-
dc.date.created2021-06-18-
dc.date.issued2001-02-
dc.identifier.issn0218-0014-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/124400-
dc.description.abstractIn this paper we consider a hidden Markov mesh random field (HMMRF) for character recognition. The model consists of a "hidden" Markov mesh random field (MMRF) and an overlying probabilistic observation function of the MMRF. Just like the 1-D HMM, the hidden layer is characterized by the initial and the transition probability distributions, and the observation layer is defined by distribution functions for vector-quantized (VQ) observations. The HMMRF-based method consists of two phases: decoding and training. The decoding and the training algorithms are developed using dynamic programming and maximum likelihood estimation methods. To accelerate the computation in both phases, we employed a look-ahead scheme based on maximum marginal a posteriori probability criterion for third-order HMMRF. Tested on a larget-set handwritten Korean Hangul character database, the model showed a promising result: up to 87.2% recognition rate with 8 state HMMRF and 128 VQ levels.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherWORLD SCIENTIFIC PUBL CO PTE LTD-
dc.subjectHIDDEN MARKOV-MODELS-
dc.subjectSPEECH RECOGNITION-
dc.subjectWORD-
dc.titleA 2-D HMM method for offline handwritten character recognition-
dc.typeArticle-
dc.contributor.affiliatedAuthorLee, SW-
dc.identifier.doi10.1142/S0218001401000757-
dc.identifier.scopusid2-s2.0-0035247075-
dc.identifier.wosid000167449100006-
dc.identifier.bibliographicCitationINTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.15, no.1, pp.91 - 105-
dc.relation.isPartOfINTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE-
dc.citation.titleINTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE-
dc.citation.volume15-
dc.citation.number1-
dc.citation.startPage91-
dc.citation.endPage105-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.subject.keywordPlusHIDDEN MARKOV-MODELS-
dc.subject.keywordPlusSPEECH RECOGNITION-
dc.subject.keywordPlusWORD-
dc.subject.keywordAuthorhidden Markov mesh random field (HMMRF)-
dc.subject.keywordAuthoroffline handwritten character recognition-
dc.subject.keywordAuthorlook-ahead technique-
dc.subject.keywordAuthorvector quantization-
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