Design and implementation of vision system for handwritten signature authentication

Abstract

In an era marked by the digital revolution, ensuring the integrity and authenticity of signatures is a pivotal concern in the realm of identity verification. This project introduces an innovative approach to address this imperative issue, harnessing the power of modern deep learning techniques to devise a robust signature authentication system. By meticulously integrating theory and practice, this endeavor seeks to transcend existing limitations and redefine the landscape of identity validation. With a core objective to enhance digital security, this project undertakes the formidable challenge of distinguishing between genuine and forged signatures. The objectives of this project encompass the assembly of a comprehensive dataset comprising both genuine and forged signature samples, the design and implementation of a convolutional neural network (CNN) model, and the meticulous evaluation of its performance against rigorous criteria. The methodology involves the intricate fusion of data preprocessing, feature extraction, and machine learning, orchestrated to facilitate the model's acquisition of intricate signature characteristics. Through a meticulous evaluation method, the proposed system is subjected to a battery of quantitative metrics, including precision, recall, and the F1-score, forming the bedrock of a comprehensive performance assessment. This multifaceted evaluation approach encompasses controlled experimentation, model optimization, and real-world deployment to capture the intricate interplay between theoretical viability and practical effectiveness. The project culminates in a compelling conclusion, wherein the system's efficacy in signature authentication is ascertained. Achieving an accuracy rate of up to 76%, this outcome underscores the project's pivotal contribution towards enhancing the accuracy and reliability of identity verification processes.