Relevance of Automatic Number Plate Recognition Systems in Vehicle Theft Detection

Smart vehicle technologies have revolutionized human life in the current era. Smart vehicles, referred to as connected and autonomous vehicles (CAV) are equipped with advanced technologies that increase their safety and security. These technologies have the potential to transform various aspects of society in terms of transformation. This research paper presents an analysis of automatic number plate recognition (ANPR) systems and a comparison at each stage in the aspect of technologies and algorithms involving computer vision. The research paper compares algorithms used for number plate recognition at various ANPR stages. ANPR is also known as the automatic license plate recognition (ALPR) system in many countries. These ANPR systems are generally used in different applications like security surveillance, traffic management, and electric toll collection systems, including law enforcement, parking enforcement, etc. Several factors can destroy the performance of ANPR systems. These factors can lead to inaccuracies in plate recognition or cause the system to fail to identify license plates correctly. Some common factors that can undermine ANPR performance include poor image quality, nonstandard plates, weather conditions, vehicle speed, plate obstructions, lighting conditions, and hardware-based constraints. These challenges make ANPR an interesting area for research. In addition to enhancing the performance of ANPR, other technologies like RFID, and GPS can be used. The paper also focuses on the number plate recognition rate after applying different algorithms. This research aimed to improve the state of knowledge of ANPR, which includes various algorithms and ANPR steps analysis for number plate detection through citing relevant previous work.