Degraded gray-scale text recognition using pseudo-2D hidden Markov models and N-best hypotheses
Graphical Models and Image Processing
An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keyword Spotting in Poorly Printed Documents using Pseudo 2-D Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
1-Dimensional and Pseudo 2-Dimensional HMMs for the Recognition of German Literal Amounts
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
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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 on 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 ovservation 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 largetst-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.