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@article{169235,
        author = {Kaustubh Srivastava and Mehul Wadhwa and Sheenam Naaz},
        title = {Automated Entry of Vehicles in Gated Areas Using License Plate Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {878-882},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169235},
        abstract = {This paper introduces an automated vehicle entry system using License Plate Recognition (LPR) technology to enhance security and streamline access control in residential and commercial gated areas. Traditional vehicle access methods, which rely on manual checks by security personnel, are slow and prone to errors. To address these challenges, the proposed system uses cameras to automatically capture vehicle license plates, reducing the need for human intervention and speeding up the process.
The system’s architecture combines advanced computer vision and Optical Character Recognition (OCR) techniques. YOLOv5, a Convolutional Neural Network (CNN)-based model, is used for fast and accurate detection of license plates, while Tesseract OCR extracts the alphanumeric characters from the detected plates. These characters are then cross-checked against a database of authorized vehicles stored in an SQL system. If a match is found, the gate opens automatically; otherwise, the system alerts security personnel. The LPR system is designed for real-time operation, providing high accuracy and speed, even in challenging conditions such as poor lighting or bad weather. It automates the vehicle entry process, reducing the burden on security staff and improving overall security. This scalable system can be adapted for broader applications such as parking lots, toll booths, and corporate premises.},
        keywords = {},
        month = {November},
        }