Comprehensive Comparative Analysis of Machine Learning Algorithms for Vehicle Number Plate Detection in Challenging Real-World Environments
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Date
2024
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Faculty of Engineering and Natural Science
Abstract
Automatic vehicle license plate detection (ALPR) plays a crucial role in various , such as traffic control, safety systems and electronic payment collection. However, the real environment creates significant problems for ALPR systems due to factors such as different lighting conditions, occlusion, poor image quality and various license plate formats. The study conducted a comprehensive comparative analysis of machine learning algorithms to determine vehicle licence plates in such complex scenarios. We explore both traditional and deep approaches to learning, assessing their strengths and weaknesses in solving real world problems. The analysis takes into account such factors as accuracy, processing speed. We hope our little research contributes to the development of such systems.
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Chazhabaev A / Comprehensive Comparative Analysis of Machine Learning Algorithms for Vehicle Number Plate Detection in Challenging Real-World Environments / 2024 / Computer Science - 7M06102