A truly 2-D hidden Markov model for off-line handwritten character recognition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, HS | - |
dc.contributor.author | Lee, SW | - |
dc.date.accessioned | 2021-09-09T12:38:59Z | - |
dc.date.available | 2021-09-09T12:38:59Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 1998-12 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/124424 | - |
dc.description.abstract | In recent years, there have been several attempts to extend one-dimensional hidden Markov model (HMM) to two-dimension. Unfortunately, the previous efforts have not yet achieved a truly two-dimensional (2-D) HMM because of both the difficulty in establishing a suitable 2-D model and its computational complexity. This paper presents a new framework for the recognition of handwritten characters using a truly 2-D model: hidden Markov mesh random held (HMMRF). The HMMRF model is an extension of a 1-D HMM to 2-D that can provide a better description of the 2-D nature of characters. The application of HMMRF model to character recognition necessitates two phases: the training phase and the decoding phase. Our optimization criterion for training and decoding is based on the maximum, marginal a posteriori probability. We also develop a new formulation of parameter estimation for character recognition. Computational concerns in 2-D, however, necessitate certain simplifying assumptions on the model and approximations on the implementation of the estimation algorithm. In particular, the image is represented by a third-order MMRF and the proposed estimation algorithm is applied over the look-ahead observations rather than over the entire image. Thus, the formulation is derived from the extension of the look-ahead technique devised for a real-time decoding. Experimental results confirm that the proposed approach offers a great potential for solving difficult handwritten character recognition problems under reasonable modeling assumptions. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.subject | SPEECH RECOGNITION | - |
dc.subject | SEGMENTATION | - |
dc.subject | WORD | - |
dc.title | A truly 2-D hidden Markov model for off-line handwritten character recognition | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, SW | - |
dc.identifier.doi | 10.1016/S0031-3203(98)00057-0 | - |
dc.identifier.scopusid | 2-s2.0-0032315082 | - |
dc.identifier.wosid | 000076648700003 | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION, v.31, no.12, pp.1849 - 1864 | - |
dc.relation.isPartOf | PATTERN RECOGNITION | - |
dc.citation.title | PATTERN RECOGNITION | - |
dc.citation.volume | 31 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 1849 | - |
dc.citation.endPage | 1864 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | SPEECH RECOGNITION | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | WORD | - |
dc.subject.keywordAuthor | hidden Markov mesh random field (HMMRF) | - |
dc.subject.keywordAuthor | off-line handwritten character recognition | - |
dc.subject.keywordAuthor | look-ahead technique | - |
dc.subject.keywordAuthor | maximum marginal a posteriori probability | - |
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