Abstract: Technologies and services geared towards smart vehicles and Intelligent Transportation Systems (ITS) continue to revolutionize many aspects of human life. This paper provides a comprehensive overview of the latest techniques and progress in Automatic Number Plate Recognition (ANPR) systems, offering a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology can detect and recognize vehicles by their number plates using recognition techniques. Even with the best algorithms, successful ANPR system deployment may require additional hardware to maximize accuracy Performance can be undermined by various factors, including the condition of the number plate, nonstandardized formats, complex scenes, camera quality and mount position, tolerance to distortion, motion blur, contrast issues, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software tools, and other hardware-based constraints.

Keywords: automatic number plate recognition, image processing, computer vision, machine learning, vehicle identification, neural networks, intelligent transportation system, smart vehicle technologies, object detection and tracking, recognition

| DOI: 10.17148/IARJSET.2024.117102