Enhanced Number Plate Recognition for Restricted Area Access Control Using Deep Learning Models and EasyOCR Integration

In today’s security landscape, the demand for dependable license plate detection systems to regulate access to restricted areas is unequivocal. This paper introduces a pioneering approach that harnesses state-of-the-art object detection models, encompassing various YOLO variants (v8, v9), and seamlessly integrates them with EasyOCR to enhance license plate recognition capabilities within restricted zones. The proposed system is meticulously engineered to bolster security protocols by swiftly and accurately identifying license plates in real-time, thereby serving as a formidable deterrent against unauthorized access attempts. Through exhaustive comparative analysis across different YOLO architectures and seamless fusion with EasyOCR, this study demonstrates the unparalleled accuracy and reliability achieved by our methodology. Comprehensive experimentation conducted on benchmark datasets underscores the efficacy of our approach, positioning it as a promising solution for integration into practical security systems, thereby establishing a new standard for license plate detection in restricted area access control.