Enabling Intelligent Traffic Systems: A Deep Learning Method for Accurate Arabic License Plate Recognition
<|reference_start|>Enabling Intelligent Traffic Systems: A Deep Learning Method for Accurate Arabic License Plate Recognition: This paper introduces a novel two-stage framework for accurate Egyptian Vehicle License Plate Recognition (EVLPR). The first stage employs image processing techniques to reliably localize license plates, while the second stage utilizes a custom-designed deep learning model for robust Arabic character recognition. The proposed system achieves a remarkable 99.3% accuracy on a diverse dataset, surpassing existing approaches. Its potential applications extend to intelligent traffic management, including traffic violation detection and parking optimization. Future research will focus on enhancing the system's capabilities through architectural refinements, expanded datasets, and addressing system dependencies.<|reference_end|>
@article{sayedelahl2024enabling,
    title={Enabling Intelligent Traffic Systems: A Deep Learning Method for
  Accurate Arabic License Plate Recognition},
    author={M. A. Sayedelahl},
    journal={FMDB Transactions on Sustainable Intelligent Networks, 1, 40-55
  (2024)},
    year={2024},
    doi={10.69888/FTSIN.2024.000156},
    archivePrefix={arXiv},
    eprint={2408.02904},
    primaryClass={cs.CV cs.AI}
}