Abstract: The rising number of automobiles has led to an increased demand for a reliable license plate identification system that can perform effectively in diverse conditions. This applies to local authorities, public organizations, and private companies in Morocco, as well as worldwide. To meet this need, a strong License Plate Recognition (LPR) system is required, taking into account local plate specifications and fonts used by plate manufacturers. This paper presents an intelligent LPR system based on the YOLOv5 framework, trained on a customized dataset encompassing multiple fonts and circumstances such as illumination, climate, and lighting. The system incorporates an intelligent region segmentation level that adapts to the plate's type, improving recognition accuracy and addressing separator issues. Remarkably, the model achieves an impressive precision rate of 99.16% on problematic plates with specific illumination, separators, and degradations. This research represents a significant advancement in the field of license plate recognition, providing a reliable solution for accurate identification and paving the way for broader applications in Morocco and beyond.

El Mehdi Ben Laoula, Marouane Midaoui, Mohamed Youssfi and Omar Bouattane, “Intelligent Moroccan License Plate Recognition System Based on YOLOv5 Build with Customized Dataset” International Journal of Advanced Computer Science and Applications(IJACSA), 14(6), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140638

@article{Laoula2023,
title = {Intelligent Moroccan License Plate Recognition System Based on YOLOv5 Build with Customized Dataset},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140638},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140638},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {6},
author = {El Mehdi Ben Laoula and Marouane Midaoui and Mohamed Youssfi and Omar Bouattane}
}