Automatic motor vehicle number plate recognition

Abstract

The purpose of this initiative is to develop automatic motor vehicle number plate recognition (Bangla) using machine learning, identifying and taking out the numbers of license plates from photos. By using this system we intend to help the traffic control system in detecting any issue within a few moments. Moreover, collecting tolls and enforcement of law can be implemented with this number plate recognition system. Various object detection models have been used in this in various suggested methods to identify and recognize number plates, optical character recognition and license plate detection make up the system’s three basic building blocks. YOLOv8, YOLOv7, YOLOv5, VGG16, RESNET50, DETR, VGG16 are the models used in this project. Object detection models are used to detect the number plate of a vehicle from the images. That is how the method will be able to successfully recognize and detect the number plate. The precision, recall and mAP value of YOLOv8 is 96.4%, 84.8%, 92.9% respectively. For YOLOv7 it is 61.1%, 46%, 46.5% respectively. For YOLOv5 it is 98.1%, 12.1%, 17.4% respectively. DETR is 6.5%, 7.5%, 8.32% respectively. For VGG16 the test accuracy is 90.14% and for ResNet50 it is 89.91%. Additionally, this system will be implemented within the web. So by using a phone camera the car number plates would be detected with a device like a mobile phone. To sum up, the number plate detection system has the ability to detect, identify and be able to save the information and will help provide a reliable management system for traffic and capturing fraud and indiscipline in the traffic control system.