A Hybrid NN/HMM Modeling Technique for Online Arabic Handwriting Recognition
This addresses the problem of accurate character recognition for Arabic handwriting users, but it is incremental as it builds on existing hybrid techniques.
The paper tackles online Arabic handwriting recognition by proposing a hybrid NN/HMM model, achieving 96.4% character recognition accuracy on the ADAB database, which is statistically significant compared to state-of-the-art systems.
In this work we propose a hybrid NN/HMM model for online Arabic handwriting recognition. The proposed system is 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 extremum 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.8