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

TL;DR

This paper introduces a novel online multilingual writer identification system combining an Extended Beta-Elliptic model, Fuzzy Elementary Perceptual Codes, and deep neural networks, demonstrating improved accuracy on Latin and Arabic scripts.

Contribution

The paper proposes a new biometric writer identification method using a novel feature extraction model and deep learning, enhancing performance over existing systems.

Findings

Achieves higher accuracy than existing systems on Latin and Arabic scripts.

Effectively segments online handwriting into Beta strokes for analysis.

Utilizes deep neural networks for improved classification.

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.