Enabling Intelligent Traffic Systems: A Deep Learning Method for Accurate Arabic License Plate Recognition

TL;DR

This paper presents a two-stage deep learning framework for highly accurate Arabic license plate recognition, combining image processing for localization and a custom model for character recognition, with applications in traffic management.

Contribution

It introduces a novel two-stage approach specifically designed for Arabic license plates, achieving high accuracy and surpassing existing methods.

Findings

99.3% accuracy on diverse dataset

Effective localization and recognition of Arabic characters

Potential for improved traffic management systems

Abstract

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.