An Extended Beta-Elliptic Model and Fuzzy Elementary Perceptual Codes for Online Multilingual Writer Identification using Deep Neural Network release_stn222k2bvb3piozn75swir2ku

by Thameur Dhieb, Sourour Njah, Houcine Boubaker, Wael Ouarda, Mounir Ben Ayed, Adel M. Alimi

Released as a article .

2018  

Abstract

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.
In text/plain format

Archived Files and Locations

application/pdf  2.0 MB
file_hyiwx34ryjbnbbxhhn6637svaq
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2018-11-10
Version   v4
Language   en ?
arXiv  1804.05661v4
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 0f86ba9a-b75c-4c8b-819e-9dc5079d3307
API URL: JSON