Hybrid Neural Network and Genetic Algorithm for off-Lexicon Online Arabic Handwriting Recognition

In this paper we propose the hybridization of neural networks and genetic algorithm for online Arabic handwriting recognition. The used method consists in decomposing the input signal into continuous parts called graphemes based on Beta-Elliptical model and baseline detection. The segmented graphemes are then described according to their position in the pseudo-word by a combination of geometric features modeling their trajectory shape and provided in the input of the neural networks used for graphemes class recognition. Finally, a genetic algorithm is used to generate the characters code corresponding to the obtained chain of recognized graphemes code by applying the genetic search process: selection, crossover and mutation. The developed system is evaluated using an Arabic words dataset extracted from the ADAB Database.