  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.
