Development of Automatic Number Plate Recognition System of Bangladeshi Vehicle Using Object Detection and OCR

The traffic issue in Bangladesh is one of the strongest and most demanding issues in city surveillance today. Finding and separating automobiles on the side of the road and in the parking lot has become more crucial as Bangladesh’s traffic congestion problem worsens at an alarming rate. There is now a lot of study being done on the topic of object detection and classification. The detection of vehicles and the recognition of license plates have been the subject of numerous studies. But there are still certain restrictions. Locating the double-row number plate accurately is one of them. In this study, we proposed a technique for locating both single-row and double-row license plates as well as detecting the vehicle type. The suggested model, YOLOv4 and OCR (optical character recognition) Tesseract can be utilized to create a real-time system and has good accuracy and inference time for a variety of illumination and style of Bangladeshi number plates. The model showed a mAP value of almost 97%, and the other evaluation metrics performance is also acceptable. Our proposed model outperformed the prior system.