SIGNATURE SCRUTINY BASED ON AI TECHNIQUES

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

The prevalence of fraudulent activities, especially in the banking sector, underscores the critical need for robust authentication mechanisms. In this project, we present a novel approach to signature scrutiny utilizing Image filters techniques. Leveraging the power of Python programming language, we have developed a graphical user interface (GUI) application that facilitates the uploading and comparison of signature images to detect forgeries. Our methodology begins with image preprocessing techniques such as filtering to enhance the clarity of signatures. Subsequently, we employ advanced algorithms to compare uploaded signatures with their authentic counterparts. Through feature extraction and comparison, the system effectively identifies discrepancies indicative of potential forgery attempts. The GUI provides a user-friendly platform for banking professionals to seamlessly upload signature images and receive prompt feedback regarding their authenticity. By integrating AI-driven scrutiny into banking applications, institutions can enhance their fraud detection capabilities and safeguard against unauthorized transactions. Overall, this project demonstrates the feasibility and effectiveness of employing image filters techniques for signature scrutiny in banking applications, contributing to the ongoing efforts to combat financial fraud and enhance security measures in the banking sector.

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Keywords

Signature Security, AI Techniques, Image Processing, Image Filtering Techniques.