A Robust Intelligent License Plate Recognition System for Vehicle Surveillance and Security Control

In recent years, the exploration of Vehicle License Plate Recognition (VLPR) has garnered substantial attention within the domain of Intelligent Transport Systems. VLPR finds diverse practical applications, including but not limited to automated parking, vehicle surveillance, toll management, security control, and traffic monitoring. This paper introduces an innovative VLPR methodology that leverages the Polar Coordinate Transformation (PCT) and the Cross-Correlation (CC) approach to enhance operational efficiency. The proposed technique involves the extraction of the license plate region from the input image utilizing an enhanced amalgamation of contour and differential algorithms. Subsequently, the PCT is employed to rectify the skewed region by calculating the license plate'sslope and appropriately adjusting its orientation. Following this, a binarization process is implemented to segment the license plate using pixel connectivity attributes, with the CC procedure facilitating the license plate recognition. The proposed technique effectively addresses various challenges, encompassing distinct fonts, colors, languages, intricate backgrounds, deformities, occlusion, and illumination variations, among others. Comparative experimental results demonstrate the superior accuracy and performance of the proposed method in comparison to state-of the-art procedures. This paper makes a significant contribution towards the development of a dependable VLPR system for a multitude of real-world applications.