<![CDATA[Automated vehicle license plate identification, critical in modern transportation systems, finds application in traffic monitoring, law enforcement, and transportation optimization. This study explores machine learning's potential to enhance accuracy and efficiency in this domain. Leveraging neural networks and pattern recognition, it aims to build an automated system robust across diverse conditions. Addressing limitations in traditional methods, it focuses on adapting to lighting, angles, and image quality variations. The societal impact includes streamlining law enforcement and optimizing traffic flow, revolutionizing transportation and surveillance. Methodologies cover data collection, ethical considerations, preprocessing, feature extraction, model selection, and iterative refinement. Ethical data handling ensures privacy compliance. Feature extraction methods like HOG, LBP, CNNs, and color histograms capture crucial aspects for identification. Model selection spans SVMs, CNNs, decision trees, and ensemble methods, considering task complexity and dataset characteristics. This study evaluates machine learning's potential for revolutionizing license plate identification systems.]]>
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