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

This paper proposes a recognition system of constrained handwritten Hangul (Korean characters) and alphanumeric characters using discrete hidden Markov models (HMM). 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, 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. Simulation results show that the proposed method recognizes handwritten Hangul and alphanumerics effectively