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Automated Vehicle License Plate (VLP) detection and recognition have ended upbeing a significant research issue as of late. VLP localization and recognitionare some of the most essential techniques for managing traffic using digitaltechniques. In this paper, four smart systems are developed to recognizeEgyptian vehicles license plates. Two systems are based on characterrecognition, which are (System1, Characters Recognition with Classical MachineLearning) and (System2, Characters Recognition with Deep Machine Learning). Theother 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 objectdetection algorithms, and machine learning based object recognition algorithms.The performance of the developed systems has been tested on real images, andthe experimental results demonstrate that the best detection accuracy rate forVLP is provided by using the deep learning method. Where the VLP detectionaccuracy rate is better than the classical system by 32%. However, the bestdetection accuracy rate for Vehicle License Plate Arabic Character (VLPAC) isprovided by using the classical method. Where VLPAC detection accuracy rate isbetter than the deep learning-based system by 6%. Also, the results show thatdeep learning is better than the classical technique used in VLP recognitionprocesses. Where the recognition accuracy rate is better than the classicalsystem by 8%. Finally, the paper output recommends a robust VLP recognitionsystem based on both statistical and deep machine learning.

Deep Machine Learning Based Egyptian Vehicle License Plate Recognition Systems