Published Paper Details:

Every country now has a serious issue with traffic regulation and vehicle ownership identification. Identifying the driver of a car that breaches traffic laws or goes too fast can be challenging at times. Due to the possibility that the traffic official may not be able to obtain the vehicle number from the moving car due to the speed of the vehicle, it is impossible to catch and penalize those types of offenders. Therefore, one of the solutions to this problem is to develop an automatic number plate recognition (ANPR) system. Today, a variety of ANPR systems are accessible. Despite the fact that these systems use various approaches, the work is still quite difficult because to things like the vehicle's rapid speed, non-uniform number plate, and language. The total rate of recognition can be significantly impacted by the quantity and type of lighting. Most systems function with these restrictions. In this work, various ANPR strategies are described while taking into account processing time, success rate, and image size as criteria. An ANPR expansion is proposed at the end of this work. Artificial Neural Network (ANN), Character Segmentation, and Automatic Number Plate Recognition (ANPR) image segmentation, optical character recognition, and license plate analysis.