Fingerprint recognition is a widely used biometric authentication method in security systems. However, distorted or poor-quality fingerprints—caused by skin conditions, pressure variation, or sensor issues—can significantly degrade recognition accuracy. This project aims to automatically detect and rectify distorted fingerprints using image processing and machine learning techniques. The system includes modules for fingerprint enhancement, distortion detection, and rectification using convolutional neural networks (CNNs). By improving fingerprint quality before feature extraction and matching, the system enhances recognition performance and reduces false rejections, particularly in high-security or forensic applications.
Railway level crossings are common points of conflict between rail and road traffic, and manual gate operations often lead to accidents due to human error or delay. This project presents an Automatic Railway Gate System that automates gate control using sensors and microcontrollers. The system detects train arrival and departure in real-time and manages the opening and closing of the gate automatically, ensuring higher safety, faster operation, and minimal human intervention.
With increasing cases of certificate and document forgery, there is a growing need for an automated and intelligent system to detect manipulated documents. This project proposes a system that leverages image processing, OCR (Optical Character Recognition), and AI-based tamper detection techniques to verify the authenticity of scanned certificates or digital documents. The system detects forgery by identifying anomalies such as inconsistent fonts, altered signatures, tampered seals/logos, or pixel-level discrepancies. It flags forged areas and generates a verification report. This system ensures the integrity of documents submitted in academic, corporate, and legal domains.
