Enhanced Automatic Number Plate Recognition for High-Speed Vehicles: Leveraging YOLO and Haar Cascade

The integration of Artificial Intelligence (AI) into advanced Automatic Number Plate Recognition (ANPR) systems offers a state-of-the-art approach for precise identification of license plates on vehicles. This system employs sophisticated techniques including template matching and connected component analysis to efficiently extract characters from input images. Plate extraction, character segmentation, and template matching are integral parts of the process, ensuring reliable operation across diverse outdoor environments with a focus on rapid identification during daylight hours. ANPR systems find wide applications in automated toll collection, parking access control, traffic law enforcement, and road traffic monitoring. Leveraging AI, the approach utilizes multiple templates and character identification methods to enhance accuracy and efficiency. Following character recognition, the identified characters are validated against a license plate database, achieving an accuracy of about 91.8%. Renowned for its simplicity and rapidity in character recognition and plate segmentation across various weather conditions, this model represents a significant advancement in high-speed ANPR technology enabled by AI.