A hybrid handwritten word recognition using self-organizing feature map, discrete HMM, and evolutionary programming

A hybrid system for the recognition of handwritten Farsi words using self-organizing feature map, right-left discrete hidden Markov models, and evolutionary programming is presented. The histogram of chain-code directions of the image strips, scanned from right to left by a sliding window, is used as feature vectors. The self-organizing feature map is used for constructing the codebook and also smoothing the observation probability distributions. A population based approach using evolutionary programming with a self-adaptive Cauchy mutation operator is used to find an appropriate initial model as starting point for the classical Baum-Welch algorithm. Experimental results were found to be promising