Optimized Python Approach for Vehicle License Plate Detection and Recognition

Abstract—Automatic license plate detection and recognition (ALPR) is a critical component of intelligent transportation systems, enabling applications such as traffic monitoring, parking management, and law enforcement. This paper presents an efficient Python-based approach that integrates image processing, computer vision, and optical character recognition (OCR) techniques for real-time license plate detection and recognition. The proposed framework employs pre-processing methods to enhance image quality, followed by contour detection and segmentation to accurately localize license plates. Subsequently, character recognition is performed using OCR to extract alphanumeric information from the detected plates. The system is evaluated on diverse datasets containing vehicles under varying lighting and environmental conditions. Experimental results demonstrate high detection accuracy, precision, recall, and F1-score while maintaining low computational overhead, making it suitable for deployment in real-world scenarios. The Python-based implementation ensures flexibility, scalability, and ease of integration with existing IoT and smart city infrastructures.