Published Paper Details:

Companies need signature fraud detection systems to prove valid signatures on official documents across various industries. Fraudulent signatures harm many different sectors by causing damage to finances and creating identity theft problems along with legal problems. Forensic experts who check signatures by hand take too long and produce results that depend on their own feelings and mistakes. Digital platforms need an automatic security system that accurately spots fake signatures because of growing document threats and rising electronic transactions. This project creates a system that uses CNNs to identify irregular signatures through deep learning technology. CNNs show remarkable results when classifying images which make them good at differentiating real signatures from frauds. The application runs on the web through a Flask framework and lets users add signature images for evaluation. The deep learning model needs processed images first which means the system prepares the images by adjusting their size and quality while removing noise for optimal performance. Our model works with diverse data to examine signature images and inform users if their input is an original or a fake. This system working model fits different industries including bank, law, business security, and official government use as they all depend on accurate signature verification. Using advanced machine learning helps our system identify signatures better which reduces document fraud risk and creates a reliable solution for organizations that need secure verification. The system can develop additional security features by adding electronic signature tracking alongside multiple security checks and block chain network connections.