An Extended Beta-Elliptic Model and Fuzzy Elementary Perceptual Codes for Online Multilingual Writer Identification using Deep Neural Network
This work addresses the problem of identifying authors from online handwriting for applications in biometrics and document analysis, presenting an incremental improvement over prior methods.
The paper tackles writer identification from online handwriting by proposing a system that uses an Extended Beta-Elliptic model and Fuzzy Elementary Perceptual Codes for feature extraction, followed by a Deep Neural Network classifier, achieving competitive results on Latin and Arabic scripts compared to existing systems.
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.