Forensic Document Examination For Handwritten Signatures Using Deep Learning

Abstract

Forgery is a type of fraud de ned 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 project, we are focusing on developing an application that aims to help forensic examiners in the process of identi cation and detection of handwritten signature forgeries inside documents in addition to the identi cation of possible types of signature forgery methods. The proposed system uses contemporary methods that utilize a deep learning approach of CNNs (Convolutional Neural Networks) based on both binary, and categorical image classi cation methodologies to help forensic examiners measure the genuineness of handwritten signatures. For the image classi cations, we have considered using a number of ve di erent CNN models which are: VGG-16, ResNet50, Inception-v3, Xception, and our 2DCNN model. The purpose of using these di erent CNN models is to conduct a comparative study between each of the models to help determine which model is more e cient at identifying images containing text data of similar resemblances, as to the nature of handwritten signatures. 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. The system aims to provide all functionalities which are required by current standards through the use of robust system architecture, design patterns, and various techniques to build a practical application that follows an expandable structure.