Smart Template Mapping and Fraud Detection

Abstract

This paper introduces the concept of a “Sample Template Mapping and Fraud Detection" system implemented for document forgery verification software that verifies a document against a pre-defined standard template. It identifies forged changes by tracking text and structural variations and finds optimal use in the applications of HR departments, banks, and law firms. With Python and OpenCV, Tkinter, and Tesseract OCR, the system can upload templates and authenticate purported documents. I treads and validates text for in consistency and cross-verifies against a registered database. Pre-processing of images such as grayscale, Gaussian blur, and noise removal improve the accuracy of OCR, while SSIM identifies structural tampering. The system is able to identify genuine and fake documents itself and identify discrepancies in real time with a simple Tk inter- based GUI. Automated fraud detection eliminates time consumed on manual checks, minimizes the chance of errors, and maximizes the efficiency. A Tesseract OCR-based system ensures accuracy, document genuineness, and compatibility with other verification systems, and therefore, it is a vital tool to combat document forgery.