Published Paper Details:

With the growth of smart transportation systems, there is a need for reliable ways to detect and read license plates automatically. This research introduces an ALPR (Automatic License Plate Recognition) system that works well even in difficult situations, like poor lighting, messy backgrounds, or unusual license plate designs. The system uses two main technologies. It finds the license plate in an image with a fast and accurate model called YOLO (You Only Look Once). After the License plate is detected then, it uses LPRNet (License Plate Recognition Network) to read the letters and numbers on the plate. These steps make the system quick and accurate. The process involves finding the license plate, breaking the characters into separate parts, and creating a text box with the results. It can handle challenges like low-quality images, dirty plates, and background noise while still working well. Tests on different datasets show that the system works in both indoor and outdoor conditions and can recognize license plates in many languages and styles. This system has many practical applications, including automatic payment collection at toll booths, traffic monitoring, law enforcement, and smart city projects, where fast and accurate vehicle identification is essential. By providing reliable results in real-time, this ALPR system helps make transportation systems more efficient and supports various operations like managing traffic and enforcing laws. By using advanced computer vision techniques, this recognition system provides an efficient solution for vehicle identification tasks.