• Volume: 1
• Issue:03
• Date: 01-03-2024

Title : A Review Analysis of Mixed Automatic License Plate Recognition Using Machine Learning Methods

Abstract: Automatic License Plate Recognition (ALPR) systems have become integral in various applications such as traffic management, law enforcement, and security systems. This review paper presents a comprehensive analysis of mixed ALPR systems employing machine learning methods. The paper surveys a wide array of ALPR techniques, including traditional machine learning algorithms and recent advancements in deep learning models. Emphasis is placed on the methodologies used for license plate detection, character segmentation, and recognition. Key challenges addressed in the review include varying lighting conditions, diverse plate formats, and image distortions. By comparing different approaches, the paper highlights the strengths and limitations of each method. The analysis also includes performance evaluations based on metrics such as accuracy, processing speed, and robustness. Findings suggest that hybrid models combining convolutional neural networks (CNNs) with traditional machine learning techniques offer promising results, particularly in complex and dynamic environments. The paper concludes with recommendations for future research directions, focusing on improving real-time processing capabilities and enhancing the generalizability of ALPR systems across different regions and conditions.

Key Words: Automatic License Plate Recognition (ALPR), Machine Learning, Deep Learning, Convolutional Neural Networks (CNNs), License Plate Detection, Character Segmentation, Optical Character Recognition (OCR)

Area: Engineering

• Approved ISSN: ----
• Paper Id: IJREISTU8
• Page No: 1-6

• Author: Pankaj Kumar Chaurasiya

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• Co- Author: Sanjay Pal