ML Based Number Plate Recognition Model using Computer Vision

In this research paper the Number Plate Recognition System (NPRS) is used which is one of the smartest and most high-tech detection systems. This is a system of different technologies where an interface enables the system to automatically detect and read the unique ID of a number plate of vehicles from the real-time captured images. It is basically the process of extracting the pixel data from the digital images and converting it into ASCII values or the plain text of the number plate. The system first detects the vehicle and then takes a real-time image of that vehicle. To get the solution to this problem, a deep learning model known as YOU ONLY LOOK ONCE (YOLO) and its latest version YOLOv8 is used for detecting the license plates. This model's main aim is to make use of different types of morphological operations so that the specifically required number plate can be detected and translated efficiently. Once we get the desired number plate, we can perform various important operations or tasks such as checking it with the databases to check for stolen vehicles, for tracking the vehicle's movement, etc. It can help to improve public safety and enhance the efficiency of the transportation system.