Enabling Intelligent Traffic Systems: A Deep Learning Method for Accurate Arabic License Plate Recognition
This work addresses license plate recognition for intelligent traffic systems in Egypt, representing an incremental improvement over existing methods.
The paper tackled the problem of Egyptian Vehicle License Plate Recognition by developing a two-stage framework with image processing for localization and a custom deep learning model for Arabic character recognition, achieving 99.3% accuracy on a diverse dataset.
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.