Comparing the Effectiveness of Different Classifiers of Data Mining for Signature Recognition System

Abstract

Handwritten signatures are commonly used for the signification, authentication, and validation of people's important transactions and documents. However, this security measure could also be a threat at the same time. This study aims to develop a model to detect and recognize a signature. This study also tries to extract, investigate and compare the use of static and dynamic features using Five well-known (5) classifiers such as AutoMLP, Naïve Bayes, Neural Network, Gradient Boosted Trees and Generalized Linear Model. The classifier that shows the most acceptable model is Neural Network which shows an accuracy rate of 92.88%.