Double Morphological Segmentation for Increasing Performance of Signature Classification Using Machine Learning Technique

Abstract

Signatures are one of the important characteristics that security needs to be considered. Some cases related to signature forgery often occur, this is certainly dangerous especially if the signature forgery can be misused. So there needs to be a verification process on the authenticity of signatures related to this. Several studies related to signature verification have been carried out, one of them using digital image processing techniques. However, some studies only propose a method without comparison of results. This study aims to compare methods and development of signature verification methods based on digital image processing with machine learning techniques. The final results of this research can later be used as a design module that can be used in system development or signature verification applications. The data used is the image of the digitization of the signature of the Lecturer in the STMIK AMIKOM Purwokerto environment. The segmentation method used in this study is adaptive maximum minimum thresholding with double morphological operation. Good segmentation results are expected to provide good classification results. Comparison of several different classifiers in the classification stage is carried out, including Linear Regression, Naïve Bayes (NB), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbor (K-NN).