Real-Time License Plate Recognition and Database Management

The increasing complexity of urban infrastructure and the growing number of vehicles, the demand for efficient and automated traffic monitoring and management systems is high. In the old license plate recognition (LPR) system, manual checks and standard OCR are problematic, and errors are likely to occur when the light, angle, and environmental conditions are not constant. To address these issues, we propose a real-time vehicle recognition pipeline for accurate license plate detection and recognition, utilizing CCTV or other input sources captured by the camera. By using the preprocessing method, the image sharpness is improved and the noise level is reduced, thereby enhancing the system's reliability. It is automatically used for help. It consists of vehicle detection, license plate position identification, text segmentation, and character recognition using the simple plate recognition technique. Logging is possible. This approach has shown good efficiency. The compatibility rate is 97.1%, the reproducibility rate is 95.4%, and the F1 score is 96.2%. This comprehensive solution enables high-speed, scalable, and high-precision recognition in the implementation of smart cities, including toll booths, security gates, parking lots, and advanced road traffic systems (ITS). Keywords— Plate Recognition (LPR), Convolutional Neural Network (CNN), Deep Learning, Optical Character Recognition (OCR), Real-Time Detection, Image Processing, Database Management, Vehicle Identification, Smart City, Intelligent Transportation System (ITS), Traffic Monitoring.