Automatic Recognition of Algerian Vehicle Registration Plates

Abstract

This thesis presents an innovative approach for the automatic recognition of vehicle license plates using the YOLO object detection model and optical character recognition. License plate detection is a crucial component of intelligent transportation systems and security applications, and this research aims to address the challenges associated with detecting Algerian vehicle plates under varying environmental conditions. The study begins by discussing the complexities involved in plate detection, including diverse plate designs, lighting variations, and other external factors. We then explore the dataset preparation, focusing on the importance of data annotation to ensure the robustness of the detection model. The proposed system integrates object detection and optical character recognition techniques to identify and read license plates. The application is specifically designed to assist Algerian law enforcement in quickly identifying stolen or missing vehicles. The results show high accuracy in detecting and recognizing vehicle plates, demonstrating the model's potential for real-world use in traffic monitoring and security.