Resumen
Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology?s importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate?s location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters? contours within the grayscale image. Third, the detected the alphanumeric characters? contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.