  Actually, the ability to identify the documents authors provides more chances
for using these documents for various purposes. In this paper, we present a new
effective biometric writer identification system from online handwriting. The
system consists of the preprocessing and the segmentation of online handwriting
into a sequence of Beta strokes in a first step. Then, from each stroke, we
extract a set of static and dynamic features from new proposed model that we
called Extended Beta-Elliptic model and from the Fuzzy Elementary Perceptual
Codes. Next, all the segments which are composed of N consecutive strokes are
categorized into groups and subgroups according to their position and their
geometric characteristics. Finally, Deep Neural Network is used as classifier.
Experimental results reveal that the proposed system achieves interesting
results as compared to those of the existing writer identification systems on
Latin and Arabic scripts.
