Abstract
The objective of this research is to design and develop an IoT-based system for automatic vehicle license plate detection and recognition, aimed at addressing challenges faced by law enforcement agencies in identifying traffic violations such as speeding red-light running, and illegal parking. Traditional manual methods are time-consuming, prone to error, and often ineffective especially in high-speed scenarios where license plates are difficult to capture clearly. To achieve this the system integrates advanced computer vision and Optical Character Recognition (OCR) technologies. Specifically, OpenCV is used for image processing and plate localization while Python-Tesseract is employed for character extraction and recognition. The system is capable of processing both images and video feeds in real time. It detects the license plate area through contour and edge detection, followed by OCR-based extraction of alphanumeric characters. The implementation is designed to be robust under varying lighting conditions, image quality angles and vehicle speeds. The results demonstrate that the system performs effectively across a wide range of real-world scenarios accurately detecting and recognizing license plates with high reliability. It successfully processes data from both live video streams and uploaded files significantly reducing the time required for manual review and increasing the accuracy of vehicle identification. The system’s performance holds up well even when the license plate is partially obscured or captured in motion. In conclusion, the proposed IoT-based license plate detection and recognition system offers a practical scalable solution for traffic enforcement and vehicle monitoring. It can be integrated with existing surveillance infrastructures or deployed independently for applications such as toll collection stolen vehicle detection parking management and access control. By automating the identification process this system enhances the speed and efficiency of traffic regulation, contributing to improved public safety and smarter urban mobility.