Farsi 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(SOFM/VQ) for reading city names in postal addresses is presented. Pre-processing techniques including binarization, noise removal and besieged in a circumferential rectangular are described. Each word image is scanned form right to left by a sliding window and from each window 20 features (4*5) are extracted. The neighbourhood information preserved in the self-organizing feature map (SOFM) was used for smoothing the observation probability distributions of trained HMMs. A separate HMM is trained by Baum Welch algorithm for each city name. A test image is recognized by finding the best match (likelihood) between the image and all of the HMM words models using forward algorithm. Experimental results show the advantages of using SOFM/HMM recognizer engine instead of conventional discrete HMM.