Holistic handwritten word recognition using discrete HMM and self-organizing feature map

A holistic system for the recognition of handwritten Farsi/Arabic words using right-left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantization 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 neighborhood information preserved in the self-organizing feature map (SOFM), was used for smoothing the observation probability distributions of trained HMMs. Experiments carried out on test samples show promising performance results