A 2-D HMM method for offline handwritten character recognition

Hee Seon Park, Bong Kee Sin, Jongsub Moon, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)91-105
Number of pages15
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number1
Publication statusPublished - 2001 Feb

Bibliographical note

Funding Information:
∗This research was supported by National Ministry of Science and Technology. yAuthor for correspondence.


  • Hidden Markov mesh random field (HMMRF)
  • Look-ahead technique
  • Offline handwritten character recognition
  • Vector quantization

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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