An Enhanced And Efficient Character Recognition System Using C

Essential technology with many applications in traffic surveillance, law enforcement, and vehicle administration is automatic license plate recognition (ALPR). With an especially focus on the methods, challenges, and outcomes, this paper offers a thorough overview of the most recent advancements in Automatic License Plate Recognition (ALPR) systems. Particularly in view of growing urbanization and increasing automotive use, the study stresses the need of Automatic License Plate Recognition (ALPR) in addressing issues including traffic congestion and vehicle theft. The fundamental components of Automatic License Plate Identification (ALPR) systems—that is, license plate detection, preprocessing, and character identification—are covered in this paper It also covers the problems these systems experience, including handling license plate deflection and various environmental conditions. Reviewing previous work on Automatic License Plate Recognition (ALPR), the paper sorts the techniques into conventional approaches and modern sequential methods. Examining several approaches including Connected Component Analysis (CCA), projection techniques, & Convolutional Neural Networks (CNNs), with an eye toward license plate identification and recognition Furthermore under investigation recently are the effectiveness of advanced algorithms including the YOLO-VOC network and Modified DeeplabV2 ResNet101 in automatic license plate recognition (ALPR). Deep learning methods also show potential.