Abstract: Major global challenges are traffic control and identification of vehicle owners. In many cases, it is not possible to identify the driver violating traffic rules or over-speeding since the traffic personnel fail to capture the license plate numbers of fast- moving vehicles. Thus, ANPR systems have emerged as the most practical solution. While there have been several different approaches to ANPR, with various methods, these still have challenges, such as high vehicle speeds, inconsistent number plate designs, variations in language, and changing lighting conditions, that all reduce the accuracy of recognition. However, most systems do work well under certain conditions. This paper explores the various approaches to ANPR, factoring in such aspects as the size of images, success rate, and processing time. Furthermore, an extension proposal seeks to enhance the performance of ANPR.

Keywords: Automatic Number Plate Recognition(ANPR), Artificial Neural Network(ANN), Character Segmentation Image Segmentation, Number Plate, Optical Character Recognition.

| DOI: 10.17148/IARJSET.2024.111125