Document Forgery Detection Using Image Processing

This study presents the development of a Document Forgery Detection System using a Flask-based web application integrated with the Roboflow API and a YOLO-based detection model. The primary objective of this system is to accurately identify and classify forged regions within document images through automated analysis. The model leverages the Roboflow cloud inference API for prediction and retrieves bounding box data representing potential forgeries(seal,stamp,signature) while the Flask backend ensures seamless communication between the client interface and the detection engine. In addition, the application includes a performance metrics module that reads model training results from CSV outputs and visualizes key evaluation indicators such as Precision, Recall, F1-Score, and mAP (Mean Average Precision). The system further facilitates visualization of evaluation plots, including the confusion matrix and precision-recall curves, providing comprehensive insights into model behavior. The implementation employs Python Flask for RESTful API construction, CSV parsing for metrics extraction, and CORS configuration for broad client compatibility. This architecture establishes a robust and modular pipeline for real-time document integrity verification, with potential applications in legal, financial, and administrative domains where document authenticity is critical