Title: Unveiling the Secrets of Handwriting with Machine Learning.

Authors: Jaya Paul; Kalpita Dutta; Anasua Sarkar; Nibaran Das; Kaushik Roy

Abstract: Identifying and verifying a person based on scanned images of their handwriting is a needful biometric application in historical document analysis, behavioural biometrics study, forensic science, access control, graphology, and copyrights management. Writer identification and verification are still challenging in offline and online handwriting recognition. Since the performances of handwriting biometric identification and verification systems depend on both the quality and types of chosen features, this is one of the most critical phases. This article represents a literature survey on offline and online biometric features used in different scripts for writer verification and identification techniques. Several previous efficient works on online and offline writer authentication methods for biometrics using cutting-edge hand-craft features in different levels of handwriting analysis like documents, paragraphs, words, and characters are analysed systematically to date for the first time in detail.

Keywords: writer verification; writer identification; offline feature; online feature; line level feature.

International Journal of Applied Pattern Recognition, 2024 Vol.8 No.1, pp.26-40

Received: 16 Nov 2023
Accepted: 16 Jan 2024
Published online: 24 Mar 2024