Unconstrained Farsi handwritten word recognition using fuzzy vector quantization and hidden Markov models

An unconstrained Farsi handwritten word recognition system based on fuzzy vector quantization (FVQ) and 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 codebook. A separate HMM is trained by modi®ed Baum±Welch algorithm for each city name. A test image is recognized by ®nding the best match (likelihood) between the image and all of the HMM word models using forward algorithm. Experimental results show the advantages of using FVQ/HMM recognizer engine instead of conventional discrete HMMs. Ó 2001 Elsevier Science B.V. All rights reserved.