Indonesian Vehicle License Plate Identification Using YoloV5 and OCR - Herlambang Kurniawan; Enny Itje Sela (SEAN Institute, 2023)
In response to escalating urbanization and vehicular density, this research addresses the imperative for public safety and traffic management. The relationship between public safety and traffic control highlights the importance of strong identification systems. This research aims to make a licence plate identification by using the YOLOv5 for adept object detection and complementing it with Optical Character Recognition (OCR), the study enhances license plate recognition precision. The result of this research on a dataset of 100, through the utilization of this approach, the research achieved a perfect accuracy rate of 100% in identifying vehicle plates, while the accuracy rate for character recognition on vehicle plates was at 90%.