Efficient Real-Time License Plate Recognition for Moroccan Vehicles Using Advanced Machine Learning Techniques

Abstract

This study presents a machine-learning model designed for real-time recognition of Moroccan license plate characters, with a strong emphasis on practical deployment and real-world applicability. Addressing the structural and environmental challenges unique to Moroccan plates, the model utilizes a custom dataset of approximately 4,000 images, capturing essential characteristics for accurate identification. The approach integrates a Haar Cascade classifier for plate detection with a K-Nearest Neighbors (KNN) classifier for character recognition, ensuring robust performance across varying lighting conditions and partial occlusions. Evaluated using the accuracy metric, the model consistently achieved a high accuracy of 99%. A key focus of this work is the step-by-step implementation, making it well-suited for resource-limited environments such as traffic management, security, and law enforcement. This study provides a practical and efficient solution that will contribute to the advancement of license plate recognition technologies in regions facing similar challenges, offering a scalable framework for broader applications.