Abstract
Handwriting process is one of the most complex processes of our biological repertory. Modelling 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. This paper deals with unconventional and conventional handwriting process characterization approaches based on the use of soft computing techniques namely the Radial Basis Function (RBF) neural networks and the use of mathematical models based on the recursive least squares algorithm. Modelling handwriting system as well as the hybrid validation of the proposed models constitutes the main contribution of this paper. The obtained simulation results of the hybrid validation models show a satisfactory agreement between responses of the developed models and the experimental Electromyographic signals (EMG) data then the efficiency of the proposed approaches. Applying the study is very interesting to elaborate a helpful system to those who suffer from physical handicaps.
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Slim, M.A., El Kastouri, M., Abdelkrim, A., Benrejeb, M. (2012). Hybrid Validation of Handwriting Process Modelling. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_10
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DOI: https://doi.org/10.1007/978-3-642-34481-7_10
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