A detailed review on license plate detection and recognition methods

License plate detection and recognition (LPDR) is crucial for intelligent transportation systems (ITS) to ensure traffic safety and control. LPDR systems are widely used in traffic monitoring, vehicle safety, vehicle-to-vehicle (V2V) communication, and reducing traffic accidents. Modern technologies like autonomous driving and traffic optimization require secured V2V communication. Vehicles can use the LPDR system to identify nearby vehicles to communicate safely. With the expansion of applications in daily life, LPDR is facing many challenges. From the simple static camera used at the parking lot entrance, the LPDR systems are currently used for dynamic recognition of vehicle license plates (LPs), which is very difficult due to camera movement, camera angle, and distance from the vehicles. Recognizing LPs from different countries using a single algorithm is challenging since different nations use different characters, and LPs comprise multiple lines. For instance, Arabic characters could be more challenging to recognize. This article provides a performance comparison of several real-time tested and simulating LPDR methods. An ideal LPDR system must eliminate the challenges arising from new applications. The LPDR system needs to be designed to work accurately on static/dynamic conditions to develop V2V communication. By categorizing existing well-known LPDR approaches into conventional and machine learning techniques, this review tried to clarify the importance of each type of method. This work aims to review LP detection, character segmentation, and character recognition algorithms and provide guidance on future trends in this area.