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A 2-D HMM method for offline handwritten character recognition

Authors
Park, HSSin, BKMoon, JLee, SW
Issue Date
2월-2001
Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
Keywords
hidden Markov mesh random field (HMMRF); offline handwritten character recognition; look-ahead technique; vector quantization
Citation
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.15, no.1, pp.91 - 105
Indexed
SCIE
SCOPUS
Journal Title
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Volume
15
Number
1
Start Page
91
End Page
105
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/124400
DOI
10.1142/S0218001401000757
ISSN
0218-0014
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.
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