A Review Paper on Automatic Number Plate Recognition using Machine Learning : An In-Depth Analysis of Machine Learning Techniques in Automatic Number Plate Recognition: Opportunities and Limitations

Abstract

The recognition of license plates, known as Automatic Number Plate Recognition (ANPR), is an important topic in the fields of smart transportation systems and image recognition. ANPR has the potential to significantly improve traffic control and safety measures in urban areas. Its primary objective is to automatically detect and recognize the characters on vehicle license plates using modern technology and image processing techniques. With the rapid increase in the number of vehicles, ANPR has become a crucial tool for traffic control and management. In recent years, ANPR technology has advanced significantly, leading to the development of protocols such as OCR, which enables the recognition of license plate locations and allows traffic surveillance to address various issues, such as criminal investigations, toll collection, speed control, and parking management. ANPR systems play a vital role in traffic control and surveillance, and several systems have been developed to recognize license plates, each with its specific advantages. The key steps involved in an ANPR system are the precise localization of the number plate, segmentation, and recognition. This paper presents an efficient and layout-independent ANPR system that can accurately detect and recognize license plates, thus enhancing traffic control and surveillance measures.