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@article{175890,
        author = {KISHORE L and KARTHIKEYAN K and MR. GNANAPRIYAN F},
        title = {License Plate Recognition with Vehicle Validation for Fraud Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4397-4403},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175890},
        abstract = {Over the past few years, the overall prevalence of counterfeit and illegal vehicle license plates has been a major challenge to transport authorities and law enforcement agencies. To combat this issue, the current paper proposes an end-to-end computer vision and deep learning-based system to detect counterfeit vehicle license plates effectively. The system begins by identifying the make and model of a vehicle using a trained deep neural network, thereby enabling verification of the physical features of the vehicle. It then identifies and crops the license plate area from the vehicle image using advanced object detection techniques. Then, Optical Character Recognition (OCR) is used to scan the alphanumeric characters on the license plate. Instead of relying on a centralized API, the system uses web scraping techniques to fetch corresponding registration information from publicly available government databases or transport portals. Any mismatch between the predicted make and model and the fetched registration information, or evidence of tampering on the license plate, triggers an automatic alert for further investigation. The end-to-end solution offers a scalable, cost-effective, and efficient solution for vehicle authentication and prevention of the spread of counterfeit license plates.},
        keywords = {Computer Vision, Deep Learning, Fake Plate Identification, Optical Character Recognition (OCR), Vehicle License Plate Detection, Vehicle Make and Model Recognition (VMMR), Vehicle Verification, Web Scraping.},
        month = {April},
        }