Deep Machine Learning Based Egyptian Vehicle License Plate Recognition Systems

TL;DR

This paper develops and compares four vehicle license plate recognition systems for Egyptian plates, demonstrating that deep learning significantly improves detection accuracy, especially for license plate localization, while classical methods excel in character recognition.

Contribution

The paper introduces four integrated systems combining classical and deep machine learning techniques for Egyptian vehicle license plate recognition, highlighting the superior performance of deep learning in detection tasks.

Findings

Deep learning improves detection accuracy by 32% over classical methods.

Classical methods outperform in Arabic character detection accuracy by 6%.

Deep learning achieves 8% higher recognition accuracy than classical techniques.

Abstract

Automated Vehicle License Plate (VLP) detection and recognition have ended up being a significant research issue as of late. VLP localization and recognition are some of the most essential techniques for managing traffic using digital techniques. In this paper, four smart systems are developed to recognize Egyptian vehicles license plates. Two systems are based on character recognition, which are (System1, Characters Recognition with Classical Machine Learning) and (System2, Characters Recognition with Deep Machine Learning). The other two systems are based on the whole plate recognition which are (System3, Whole License Plate Recognition with Classical Machine Learning) and (System4, Whole License Plate Recognition with Deep Machine Learning). We use object detection algorithms, and machine learning based object recognition algorithms. The performance of the developed systems has been tested on real images, and the experimental results demonstrate that the best detection accuracy rate for VLP is provided by using the deep learning method. Where the VLP detection accuracy rate is better than the classical system by 32%. However, the best detection accuracy rate for Vehicle License Plate Arabic Character (VLPAC) is provided by using the classical method. Where VLPAC detection accuracy rate is better than the deep learning-based system by 6%. Also, the results show that deep learning is better than the classical technique used in VLP recognition processes. Where the recognition accuracy rate is better than the classical system by 8%. Finally, the paper output recommends a robust VLP recognition system based on both statistical and deep machine learning.