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.
| Original language | English |
|---|---|
| Pages (from-to) | 91-105 |
| Number of pages | 15 |
| Journal | International Journal of Pattern Recognition and Artificial Intelligence |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2001 Feb |
Bibliographical note
Funding Information:∗This research was supported by National Ministry of Science and Technology. yAuthor for correspondence.
Keywords
- 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