A Software-Driven License Plate Recognition System for VIP Parking Utilizing HRNet and LPRNet with OpenCV and OCR

The fully software-driven License Plate Recognition (LPR) system aimed at managing VIP parking access, leveraging state-of-the-art computer vision techniques. The system employs HRNet for accurate real-time detection of license plates and utilizes LPRNet for efficient alphanumeric extraction of characters. By integrating OpenCV and Tesseract Optical Character Recognition (OCR), the solution eliminates the need for specialized hardware, allowing seamless integration with standard camera systems. Advanced image preprocessing methods, including adaptive thresholding, morphological transformations, and noise reduction, significantly enhance text recognition accuracy across varying environmental conditions such as lighting, occlusion, and motion blur. Furthermore, the system incorporates machine learning techniques for validating VIP access by matching the extracted license plate characters with an established database. Experimental evaluations demonstrate the system's high precision, with a recognition accuracy exceeding 96% under controlled conditions and 85% in more challenging environments, such as low-light scenarios. This scalable and cost-efficient solution paves the way for broader applications in automated access control, intelligent transportation systems, and urban mobility infrastructure, demonstrating potential for large-scale deployment in smart city environments. The proposed system reduces operational costs by eliminating the need for specialized hardware and offers easy scalability for integration with existing infrastructure. Future enhancements include the continued development of deep learning-based detection algorithms, particularly leveraging HRNet and LPRNet, positioning this solution as a key component in smart parking and automated vehicle management systems.