@article{(Open Science Index):https://publications.waset.org/pdf/9999920,
	  title     = {Handwriting Velocity Modeling by Artificial Neural Networks},
	  author    = {Mohamed Aymen Slim and  Afef Abdelkrim and  Mohamed Benrejeb},
	  country	= {},
	  institution	= {},
	  abstract     = {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.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {8},
	  number    = {12},
	  year      = {2014},
	  pages     = {2159 - 2165},
	  ee        = {https://publications.waset.org/pdf/9999920},
	  url   	= {https://publications.waset.org/vol/96},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 96, 2014},
	}