Forensic Handwritten Signature Identification Using Deep Learning

Abstract

Forgery is a type of fraud defined as the act of forging a copy or an imitation of a document, signature, or banknote which is considered a form of illegal criminal activity. In this paper, we are focusing on the identification and detection of handwritten signature forgeries inside documents. The proposed system uses contemporary methods that utilize a deep learning approach of CNNs (Convolutional Neural Networks) for binary image classification and aims to help forensic examiners measure the genuineness of handwritten signatures. We considered using a number of five different classification models of CNN which are, VGG-16, ResNet50, Inception-v3, Xception, and Our CNN model. The purpose for using these different CNN models is to determine and study which model is best at identifying images containing text data containing similar resemblances. Upon comparing these CNN models, we concluded that the ResNet50 model was able to reach the highest score at identifying handwritten signatures with an accuracy of 82.3% and 86% when tested on datasets of 300 images and 140 images respectively. Regarding future work, this is a required step that determines what model to focus on for more in-depth analysis and classification of the characteristics of handwritten signatures.