A 2-D HMM method for offline handwritten character recognition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, HS | - |
dc.contributor.author | Sin, BK | - |
dc.contributor.author | Moon, J | - |
dc.contributor.author | Lee, SW | - |
dc.date.accessioned | 2021-09-09T12:34:42Z | - |
dc.date.available | 2021-09-09T12:34:42Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2001-02 | - |
dc.identifier.issn | 0218-0014 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/124400 | - |
dc.description.abstract | In 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.language | English | - |
dc.language.iso | en | - |
dc.publisher | WORLD SCIENTIFIC PUBL CO PTE LTD | - |
dc.subject | HIDDEN MARKOV-MODELS | - |
dc.subject | SPEECH RECOGNITION | - |
dc.subject | WORD | - |
dc.title | A 2-D HMM method for offline handwritten character recognition | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, SW | - |
dc.identifier.doi | 10.1142/S0218001401000757 | - |
dc.identifier.scopusid | 2-s2.0-0035247075 | - |
dc.identifier.wosid | 000167449100006 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.15, no.1, pp.91 - 105 | - |
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 | 15 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 91 | - |
dc.citation.endPage | 105 | - |
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 | HIDDEN MARKOV-MODELS | - |
dc.subject.keywordPlus | SPEECH RECOGNITION | - |
dc.subject.keywordPlus | WORD | - |
dc.subject.keywordAuthor | hidden Markov mesh random field (HMMRF) | - |
dc.subject.keywordAuthor | offline handwritten character recognition | - |
dc.subject.keywordAuthor | look-ahead technique | - |
dc.subject.keywordAuthor | vector quantization | - |
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