Hidden Markov mesh random field: theory and its application to handwritten character recognition

Author

Park, Hee-Seon ; Lee, Seong-Whan

Author_Institution

Dept. of Comput. Sci., Chungbuk Nat. Univ., Chungbuk, South Korea

Volume

1

fYear

1995

fDate

14-16 Aug 1995

Firstpage

409

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

Keywords

character recognition; decoding; handwriting recognition; hidden Markov models; image recognition; parameter estimation; a posteriori probability criterion; handwritten character recognition; hidden Markov mesh random field; hidden Markov mesh random field model; image decoding; image modeling and recognition; parameter estimation; statistical model; Application software; Automatic speech recognition; Character recognition; Computer science; Decoding; Handwriting recognition; Hidden Markov models; Image processing; Parameter estimation; Speech analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on

Conference_Location

Montreal, Que.

Print_ISBN

0-8186-7128-9

Type

conf

DOI

10.1109/ICDAR.1995.599024

Filename

599024