AN ENHANCED AND EFFICIENT CHARACTER RECOGNITION SYSTEM USING C

Abstract

Automatic License Plate Recognition (ALPR) is an essential technology that has many uses in automobile administration, traffic surveillance, and law enforcement. This article provides a comprehensive summary of the latest progress in Automatic License Plate Recognition (ALPR) systems, with a specific emphasis on the approaches, difficulties, and results. The research emphasizes the importance of Automatic License Plate Recognition (ALPR) in tackling problems like traffic congestion and vehicle theft, especially in the context of expanding urbanization and rising automotive use. This article discusses the essential elements of Automatic License Plate identification (ALPR) systems, which include license plate detection, preprocessing, and character identification. It also addresses the issues faced by these systems, such as dealing with different environmental circumstances and license plate deflection.The report examines previous studies on Automatic License Plate Recognition (ALPR), classifying the strategies into traditional approaches and contemporary sequential methods. The examination focuses on several methods for identifying and recognizing license plates, such as Connected Component Analysis (CCA), projection techniques, and Convolutional Neural Networks (CNNs). In addition, recent research has examined the efficacy of deep learning approaches and sophisticated algorithms, such as the YOLO-VOC network and Modified DeeplabV2 ResNet101, in automatic license plate recognition (ALPR) application.