RBF neural networks for handwriting process modelling

Handwriting process is one of the most complex processes of our biological repertory. Modelling of such process remains difficult to implement. Several approaches were proposed in the literature. However, the validation results of these models remain less or more satisfactory and the basic models were the subject of improvement in the objective to approach reality as much as possible. This paper deals with new unconventional handwriting process characterization approaches based on the use of soft computing techniques namely the exploitation of artificial neural networks and more precisely the Radial Basis Function (RBF) neural networks. The obtained simulation results show a satisfactory agreement between responses of the developed RBF neural model and the experimental electromyographic signals (EMG) data for various letters and forms then the efficiency of the proposed approaches.