AN INTELLIGENT SYSTEM FOR IRAQI ARABIC LICENSE PLATE RECOGNITION USING YOLO AND MACHINE LEARNING

Neaam M.Isaam  Abdul-Sattar

doi:10.30572/2018/KJE/170225

Authors

Neaam M.Isaam Abdul-Sattar Computer Technique Engineering Department, Northern Technical University, Mosul-Iraq

DOI:

https://doi.org/10.30572/2018/KJE/170225

Keywords:

License Plate Recognition (LPR), YOLO, Optical Character Recognition (OCR), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Iraqi Car Plate, Machine Learning, Deep Learning, Vehicle Classification

Abstract

Vehicle identification processes through traffic management systems depend heavily on automatic license plate recognition capabilities. The presented system uses advanced methods from deep learning and machine learning to recognize Iraqi vehicle license plates. The system uses the Tkinter-developed graphical user interface alongside YOLO for detection and EasyOCR for text extraction together with the SVM and KNN classifiers for plate classification. The system successfully divides license plates into important parts which enables the identification of governmental entities alongside vehicle class types. The evaluation metrics reveal outstanding classification performance because both SVM and KNN models delivered 100% accuracy together with 100% precision, recall and F1-score. The proposed approach performs well when compared to existing studies because its analysis demonstrates both strength and effectiveness. The obtained findings demonstrate that the system can efficiently serve real-world needs in vehicle identification and law enforcement and intelligent transportation systems

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