Advancing Automatic Number Plate Recognition: Insights, Innovations, and Future Directions

This study explores the advancements and future prospects of Automatic Number Plate Recognition (ANPR) technology. By integrating state-of-the-art algorithms such as YOLO for vehicle detection, Custom YOLO for precise number plate localization, DeepSort for vehicle tracking, and Tesseract OCR for character recognition, the research elucidates optimal ANPR system design and implementation strategies. Findings reveal significant contributions to law enforcement, traffic management, and surveillance domains, enhancing security, traffic flow, and resource utilization. The study underscores the importance of algorithm optimization, data augmentation, and adaptive learning for advancing ANPR technology. Looking forward, opportunities lie in hardware acceleration, multimodal fusion, and ethical considerations to further elevate ANPR efficacy and address societal concerns. Collaboration across academia, industry, and government sectors will drive continued innovation, enabling safer and smarter communities in the digital age.