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@article{164743,
        author = {Swapnil Meshram and Prajwal Patil and Tejas Pinge and Mayur Sherki and Prof  Bhagyashree Karmarkar},
        title = {NUMBER PLATE TRACKING SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1571-1573},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164743},
        abstract = {This paper presents the design and implementation of an automated Number Plate Tracking System (NPTS) using advanced image processing and machine learning techniques. The system captures images or video frames from surveillance cameras and processes them to detect and recognize vehicle number plates accurately and efficiently. Key components include image acquisition, pre-processing, plate localization, character segmentation, and optical character recognition (OCR) using convolutional neural networks (CNNs). Evaluations on diverse datasets under various conditions demonstrate high accuracy and robustness, making the NPTS suitable for real-world applications such as vehicle registration, law enforcement, and toll collection. This work outlines a reliable methodology for developing NPTS and suggests potential future improvements through advanced machine learning models and larger datasets.},
        keywords = {Number Plate Tracking, Image Processing, Machine Learning, Optical Character Recognition, Convolutional Neural Networks, Vehicle Registration, Law Enforcement, Toll Collection.},
        month = {},
        }