An Efficient License Plate Recognition Model Through Deep Learning Integration with YOLO and OCR Techniques

In this paper, a two-step approach for Number Plate Recognition (NPR) was developed, leveraging the capabilities of You Only Look Once (YOLO) for efficient object detection and EasyOCR for accurate alphanumeric extraction. YOLO played a pivotal role in swiftly detecting and localizing vehicle number plates, forming a robust foundation for subsequent processing. The application of EasyOCR followed, facilitating precise character recognition on the localized plates and resulting in high-quality alphanumeric extraction. Our methodology, encompassing YOLO followed by EasyOCR and harnessing a dataset from Roboflow, yielded noteworthy results, underscoring the efficacy of this sequential approach in achieving reliable NPR. This combination of YOLO, EasyOCR, and the diverse dataset contributed to accurate and efficient recognition of vehicle number plates, showcasing the potential for real-world applications. This study contributes to the advancement of NPR systems by presenting a streamlined yet effective solution for automated license plate recognition, enriched by the inclusion of a comprehensive dataset from Roboflow.