A Comparative Analysis of Machine Learning Based Frameworks for Handwritten Signature Verification
Ritika Ritika, Dalip · 2024
Handwritten signature verification is pivotal in biometrics, security, and finance and can be applied across legal, governmental, and commercial sectors. Recent strides in machine learning techniques have transformed this field, enhancing traditional methods. This research study provides a comprehensive overview of the existing frameworks for automatic handwritten signature verification systems. Further, comparative analysis of selected studies has been provided with particular attention paid to preprocessing strategies, feature extraction techniques, and verification models used in the creation of these systems. Various databases used in the literature are evaluated based on some parameters in order to determine the most suitable framework for a particular dataset. The paper also outlines the future directions for developing an advanced verification system that can work well irrespective of the language and style of signatures. Thus, it provides insights to the researchers, which inspire further innovation and advancements in this domain.