Off-line recognition of large-set handwritten characters with multiple hidden Markov models

Abstract

There are many uncertainties in handwritten character recognition. Stochastic modeling is a flexible and general method for modeling such problems and entails the use of probabilistic models to deal with uncertain or incomplete information. This paper presents an efficient scheme for off-line recognition of large-set handwritten characters in the framework of stochastic models, the first-order hidden Markov models (HMMs). To facilitate the processing of unconnected patterns and patterns with isolated noises, four types of feature vectors based on the regional projection contour transformation (RPCT) are employed. The recognition system consists of two phases. For each character, in the training phase, multiple HMMs corresponding to different feature types of RPCT are built. In the classification phase, the results of individual classifiers to produce the final recognition result for an input character are integrated, where each individual HMM classifier produces one score that is the probability of generating the test observation sequence for each character model. In this paper, several methods for integrating the results of different classifiers are considered so that a better result could be obtained. In order to verify the effectiveness of the proposed scheme, the most frequently used 520 types of Hangul characters in Korea have been considered in the experiments. Experimental results indicate that the proposed scheme is very promising for the recognition of large-set handwritten characters with numerous variations.