A low cost IoT-based Arabic license plate recognition model for smart parking systems

License Plate Recognition is one of the significant enablers that can be utilized in wide range of applications in ITS and smart cities. The proposed design relies on three image processing stages to achieve license plate identification with high accuracy which are pre-processing, segmentation, and character recognition. The canny edge detection method with various thresholds, contour detection, and masking techniques are used to locate the car edges and license plate. In the experiment presented in this paper, 200 images were used to identify Egyptian car plates. The model successfully identified Arabic license plates with 93% accuracy. A prototype is implemented using ESP32 Cameras and Raspberry-Pi to test the system's performance. Moreover, a database and a website are hosted on the RPi to allow users to search for their car location in the parking lot using the car's full or partial license plate which was saved in database upon detection.