Off-line recognition of handwritten Korean and alphanumeric characters using hidden Markov models

This paper proposes a recognition system of constrained Handwritten Hangul (Korean character) and alphanumeric characters using discrete hidden Markov models (HMMs). The HMM process encodes the distortion and similarity among patterns of a class through a doubly stochastic approach. Characterizing the statistical properties of characters using selected features, a recognition system can be implemented by absorbing possible variations in the form. Hangul shapes are classified into six types by fuzzy inference and their recognition based on quantized features is performed by optimally ordering features according to their effectiveness in each class. The constrained alphanumerics recognition is also performed using the same features employed in Hangul recognition. The forward-backward, Viterbi and Baum-Welch reestimation algorithms are used for training and recognition of handwritten Hangul and alphanumeric characters. The simulation result shows that the proposed method recognizes effectively handwritten Korean characters and alphanumerics.