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.
|Number of pages||15|
|Journal||International Journal of Pattern Recognition and Artificial Intelligence|
|Publication status||Published - 2001 Feb|
Bibliographical noteFunding 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
- Computer Vision and Pattern Recognition
- Artificial Intelligence