Off-line recognition of isolated Persian handwritten characters using multiple Hidden Markov Models

In this paper a new method for off-line recognition of isolated handwritten Persian characters based on hidden Markov models (HMMs) is proposed. In the proposed system, document images are acquired in 300-dpi resolution. Multiple filters such as median and morphological filters are utilized for noise removal. The features used in this process are methods based on regional projection contour transformation (RPCT). In this stage, two types of feature vectors, based on this technique, are extracted. The recognition system consists of two stages. For each character in the training phase, multiple HMMs corresponding to different feature vectors are built. In the classification phase, the results of the individual classifiers are integrated to produce the final recognition.