The rapid growth of intelligent transportation systems has increased the need for automated vehicle identification solutions that are both cost-effective and easy to deploy. Traditional license plate recognition systems rely on high-resolution CCTV cameras and powerful processing units, which makes them expensive and unsuitable for small-scale or portable applications. To overcome these limitations, this project proposes a compact and economical License Plate Recognition (LPR) system using the ESP32-CAM module. The ESP32-CAM is an IoT-based microcontroller equipped with an OV2640 camera, onboard Wi-Fi, and sufficient computing capability to capture real-time images. In the proposed system, ESP32-CAM continuously monitors the vehicle's presence, captures an image at the correct moment, and transmits it wirelessly to a server or computer for further processing. The backend system applies image preprocessing techniques—such as grayscale conversion, noise reduction, edge detection, and contour analysis—to isolate the license plate region. Optical Character Recognition (OCR) is then used to extract alphanumeric characters from the detected plate. This approach significantly reduces hardware cost, wiring complexity, and power consumption compared to conventional surveillance-based LPR systems. The designed setup is highly scalable and can be deployed in applications such as automated parking systems, gated community authentication, security checkpoints, toll management, and vehicle tracking solutions. The project demonstrates the potential of integrating embedded camera modules with machine learning-based OCR algorithms to create an accurate, portable, and low-power license plate recognition system. The results confirm that the ESP32-CAM can serve as a reliable foundation for intelligent vehicular monitoring in both academic research and practical field implementations.
S et al. (Thu,) studied this question.