Published Paper Details:

Automatic Vehicle Number Plate Recognition (ANPR) systems are widely used in intelligent transportation, traffic surveillance, toll automation, and security monitoring. This research presents the design and implementation of a Raspberry Pi-based Vehicle Number Plate Recognition Detection System using image processing and machine learning techniques. The proposed system utilizes a Raspberry Pi 4 integrated with a camera module to capture real-time vehicle images. OpenCV is employed for image preprocessing, edge detection, and contour extraction to localize the number plate region. Character segmentation is performed using morphological operations, and Optical Character Recognition (OCR) techniques are applied for alphanumeric character recognition. The system is developed using Python and optimized to run efficiently on a low-cost embedded platform. Experimental results demonstrate satisfactory detection and recognition accuracy under varying lighting and environmental conditions. The proposed model provides a cost-effective, portable, and real-time solution suitable for smart parking systems, toll plazas, institutional entry management, and traffic law enforcement. The integration of embedded computing with computer vision enhances automation while reducing human intervention, making the system a practical approach for intelligent transportation infrastructure development.