Automatic Number Plate Detection With Yolov5 and OCR Methods

Automatic Number Plate Detection (ANPD) is crucial for contemporary roadway management and law enforcement functions. Nevertheless, accuracy and efficiency issues frequently plague existing ANPD approaches. This study presents a unique approach to enhance ANPD performance by integrating the YOLOv5 object detection model with Optical Character Recognition (OCR) techniques. With its real-time processing potential, YOLOv5 facilitates rapid and precise plate localization. Following this, OCR methods eliminate characters that are alphanumeric from the plates so that precise identification and interpretation are possible. The combination of YOLOv5 and OCR provides numerous benefits. First of all, YOLOv5 guarantees rapid and exact plate localization, making it possible to handle large data sets efficiently and quickly in real time. Second, OCR algorithms guarantee reliable identification of characters under difficult circumstances like different lighting and plate orientations. In practical situations, this method greatly increases both precision and dependability by optimizing the performance of the ANPD system. The suggested method’s effectiveness is demonstrated by the experimental results, which also show that it has the potential to enhance the traffic surveillance and law enforcement capabilities of ANPD systems. Overall, this research expands ANPD technology through presenting a practical solution for increasing both precision and efficiency in number plate detection technologies.