Hidden markov mesh random fiehd: Theory and its application to handwritten character recognition

Hee Seon Park, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

In recent years, there have been some attempts to extend one-dimensional hidden Markov model (HMM) to two-dimensions. This paper presents a new statistical model for image modeling and recognition under the assumption that images can be represented by a third-order hidden Markov mesh random field (HMMRF) model. We focus on two major problems: image decoding and parameter estimation. A solution to these problems is derived from the scheme based on a maximum, marginal a posteriori probability criterion for the third-order HMMRF model. We also attempt to illustrate how theoretical results of HMMRF models can be applied to the problems of handwritten character recognition.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995
PublisherIEEE Computer Society
Pages409-412
Number of pages4
ISBN (Electronic)0818671289
DOIs
Publication statusPublished - 1995 Jan 1
Event3rd International Conference on Document Analysis and Recognition, ICDAR 1995 - Montreal, Canada
Duration: 1995 Aug 141995 Aug 16

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume1
ISSN (Print)1520-5363

Conference

Conference3rd International Conference on Document Analysis and Recognition, ICDAR 1995
CountryCanada
CityMontreal
Period95/8/1495/8/16

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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    Park, H. S., & Lee, S. W. (1995). Hidden markov mesh random fiehd: Theory and its application to handwritten character recognition. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 (pp. 409-412). [599024] (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ICDAR.1995.599024