A hybrid MLPNN/HMM recognition system for online Arabic Handwritten script

Online Handwriting Recognition is still of interest with the big demand on the nomadic computers and the pen based interfaces. For the Arabic language, it is far to be claimed as a solved problem. This paper presents an online Arabic Handwriting Recognition System based on Hidden Markov Models (HMMs) and Multi Layer Perceptron Neural Networks (MLPNNs). The input signal is segmented to continuous strokes called segments based on the Beta-Elliptical strategy by inspecting the extremums points of the curvilinear velocity profile. A neural network trained with segment level contextual information is used to extract class character probabilities. The output of this network is decoded by HMMs to provide character level recognition. In evaluations on the ADAB database, we achieved 96.4% character recognition accuracy that is statistically significantly important in comparison with character recognition accuracies obtained from state-of-the-art online Arabic systems.