Handwriting Velocity Modeling by Artificial Neural Networks
The handwriting is a physical demonstration of a complex cognitive process learnt by man since his childhood. People with disabilities or suffering from various neurological diseases are facing so many difficulties resulting from problems located at the muscle stimuli (EMG) or signals from the brain (EEG) and which arise at the stage of writing. The handwriting velocity of the same writer or different writers varies according to different criteria: age, attitude, mood, writing surface, etc. Therefore, it is interesting to reconstruct an experimental basis records taking, as primary reference, the writing speed for different writers which would allow studying the global system during handwriting process. This paper deals with a new approach of the handwriting system modeling based on the velocity criterion through the concepts of artificial neural networks, precisely the Radial Basis Functions (RBF) neural networks. The obtained simulation results show a satisfactory agreement between responses of the developed neural model and the experimental data for various letters and forms then the efficiency of the proposed approaches.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1097237Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1912
 D. Van Der Gon, J. P. Thuring and J. Strackee, “A handwriting simulator,” Physics in Medical Biology, pp. 407-414, 1962.
 J. S. MacDonald, “Experimental studies of handwriting signals,” Ph. D. Dissertation, Mass. Inst. Tech. Cambridge, 1964..
 M. Yasuhara, “Experimental studies of handwriting process,” Rep. Univ. Electro- Comm. Japan, 25-2, pp. 233-254, 1975.
 S. Edelman and T. Flash, “A model of handwriting, Biological Cybernetics,” vol. 57, pp. 25-36, 1987.
 M. Sano, T. Kosaku and Y. Murata, “Modeling of Human Handwriting Motion by Electromyographic Signals on Forearm Muscles,” CCCT’03. Orlando- Florida, 2003.
 M. Benrejeb, A. El Abed-Abdelkrim and M. Sano, “Sur l’étude du processus d’écriture à la main. Approches classiques et non conventionnelles,” Revue e-STA, vol. 3, No. 1, Premier trimestre 2006, (in French).
 A. Abdelkrim, “Contribution à la modélisation du processus d’écriture à la main par approches relevant du calcul évolutif,” Thèse de Doctorat, ENIT. Tunis, 2005, (in French).
 A. Abdelkrim, M. Benrejeb and M. Sano, “PAW handwriting neural system,” International Conference on Communication, Computer and Power (ICCCP'01), IEEE/IEE Conference, Muscat, pp. 207-211, Feb. 2001.
 M.A. Slim, A. Abdelkrim and M. Benrejeb, “Handwriting process modelling by artificial neural networks,” International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM), Vol. 5, pp. 297-307, 2013.
 R. Plamondon, “A kinematics theory of rapid human movements. Part I : Movement representation and Generation,” Biological Cybernetics, Vol. 72, pp. 295-307, 1995.
 R. Plamondon, “A kinematics theory of rapid human movements. Part II : Movement time and control,” Biological Cybernetics, Vol. 72, pp. 309- 320, 1995.
 M. A. Alimi, “Contribution au développement d'une théorie de génération de mouvements simples et rapides. Application au manuscrit,” Thèse de Doctorat, Université de Montréal, Canada,1995, (in French).
 J. V. Basmajian and C. J. De Luca, “Muscles alive: their function revealed by electromyography,” Baltimore: Williams & Wilkins, 1985.
 C. J. De Luca, “Myoelectrical manifestations of localized muscular fatigue in humans,” Crit. Rev. Biomed. Eng., 11(4), pp. 251-279, 1985.
 R. Merletti, A. Rainoldi and D. Farina, “Surface electromyography for noninvasive characterization of muscle,” Execs. Sport. Sci. Rev., 29(1), pp. 20-25, 2001.
 F. De Coulon, “théorie et traitement des signaux,” Presses Polytechniques Romandes, vol.6, de Lausane, 1984, (in French).
 R. Plamondon, “What does differential geometry tell us about handwriting generation?, ” Proc. of the Third International Symposium on Handwriting and Computer Applications, Montréal, pp. 11-13, 1987.
 Z. R. Yang, “A Novel Radial Basis Function Neural Network for Discriminant Analysis,” IEEE Trans. on Neural Networks, vol. 17, Issue: 3, pp. 604-612, May 2006.
 P. Borne, M. Benrejeb and J. Haggège, Les réseaux de neurones. Présentation et application. Ed. Technip, Paris, 2007, (in French).
 J. Moody and P. J. Antsaklis, “The dependence identification neural network construction algorithm,” IEEE Trans. on Neural Networks, vol. 7, pp. 3-15, 1996.
 A. Sifaoui, A. Abdelkrim, S. Alouane and M. Benrejeb, “On new RBF neural network construction algorithm for classification,” In Proceedings of the Studies in Informatics and Control, SIC, vol. 18, No. 2, pp.103- 110, 2009.
 M.A. Slim, A. Abdelkrim and M. Benrejeb, “RBF neural networks for handwriting process modelling”. In Proceedings of the Third International Conference on Soft Computing and Pattern Recognition (SoCPaR2011), IEEE Conference, pp. 384-389, Dalian, China, 2011.