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In this work we propose a hybrid NN/HMM model for online Arabic handwritingrecognition. The proposed system is based on Hidden Markov Models (HMMs) andMulti Layer Perceptron Neural Networks (MLPNNs). The input signal is segmentedto continuous strokes called segments based on the Beta-Elliptical strategy byinspecting the extremum points of the curvilinear velocity profile. A neuralnetwork trained with segment level contextual information is used to extractclass character probabilities. The output of this network is decoded by HMMs toprovide character level recognition. In evaluations on the ADAB database, weachieved 96.4% character recognition accuracy that is statisticallysignificantly important in comparison with character recognition accuraciesobtained from state-of-the-art online Arabic systems.8

A Hybrid NN/HMM Modeling Technique for Online Arabic Handwriting Recognition