Published Paper Details:

Automatic Vehicle License Number Plate Recog nition (VLNPR) has become a vital component of intelligent transportation systems due to the rapid increase in vehicles, traffic violations, and road safety concerns. Manual traffic monitoring is inefficient, error-prone, and unsuitable for real time enforcement in high-density traffic environments. This paper presents a real-time Vehicle License Number Plate Recognition system using OpenCV and Optical Character Recognition (OCR) techniques to automatically detect, recognize, and process vehicle license plates from live video streams. The proposed system captures video input using a camera, preprocesses each frame to enhance image quality, and localizes the license plate region using computer vision techniques. EasyOCR is employed to accurately extract alphanumeric characters from the detected license plates. The recognized license numbers are then verified against a MySQL database to retrieve vehicle owner details and track violation history. In the event of a traffic violation, such as overspeeding or rule infringement, the system automatically records the violation and sends real-time SMS notifications to the registered vehicle owner using the Twilio API. Location-based information and timestamp details are also integrated to provide contextual awareness of the violation. Experimental evaluation demonstrates that the proposed system achieves reliable license plate detection and recognition accuracy under standard lighting conditions while maintaining real-time performance. The system is cost-effective, scalable, and suitable for applications such as traffic law enforcement, parking management, toll collection, and smart city surveillance. This work highlights the practical implementation of an automated, efficient, and intelligent traffic monitoring solution using open-source technologies.