Off-line unconstrained Farsi handwritten word recognition using fuzzy vector quantization and hidden Markov word models

An unconstrained Farsi handwritten word recognition system based on fuzzy vector quantization (FVQ) and a hidden Markov model (HMM) for reading city names in postal addresses is presented. Preprocessing techniques including binarization, noise removal, slope correction and baseline estimation are described. Each word image is represented by its contour information. The histogram of chain code slopes of the image strips (frames), scanned from right to left by a sliding window, is used as feature vectors. Fuzzy c-means (FCM) clustering is used for generating a fuzzy code book. A separate HMM is trained by a modified 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 work models using a forward algorithm. Experimental results show the advantages of using an FVQ/HMM recognizer engine instead of conventional discrete HMMs