Online handwritten recognition based on hidden Markov mode

In this paper an approach based on hidden Markov model for recognizing online Farsi characters is presented. At first by obtaining the number of parts of a written character and recognizing its delayed strokes، the number of candidates is decreased and then the body and delayed strokes of unrecognized character are preprocessed. The stage of preprocessing is consisting of size normalization and resampling. Thus the recognition process will be robust to transition and scaling and the extracted features will be done more precisely. The extracted features are both local and structural features. The local features are consisting of the angles between the fitted vectors to some important points of unrecognized character and the structural features are consisting of cusp، left-hump and right-hump points. The training process of models is done by Baum-Welch algorithm with a post process on it. Using the mentioned stages has the advantage of doing the recognition process in an unconstrained and writer independent manner. The obtained results show the 97. 22% precision in training and 94. 9% precision in testing experiments.