| dc.description.abstract | License plate recognition systems are widely used, such as electronic parking management systems, traffic violation monitoring systems, and stolen vehicle systems. Most of the solutions are used typical license plate recognition algorithms, which are processed through image analysis techniques, which are mainly in three stages, including license plate localization, character segmentation, and character recognition. These methods have been developed for many years, and their recognition rate has been continuously improved and optimized. But all of them have two important prerequisites. First, the license plate must be clear, and there must be no fouling, and the light source must be uniform. Second, the license plate can’t be too skewed so that the angle and position of shooting view are usually the same. Otherwise the license plate detection and character segmentation will be seriously affected and then cause recognition difficulty. Furthermore, current license plate recognition uses GPU (Graphics Processing Unit) operation and high-end hardware equipment, making the cost too expensive. In order to solve these three points, our research use a model “Tiny YOLOv3” (You Only Look Once). This model is a convolutional neural network based on the deep learning in machine learning. It uses convolutional layers to get the features of object and then achieve the effect of recognition. We use a total of two models in the research. Detecting license plate from the image in the first model, and then use performs image processing and character segmentation from the detected license plates, and sends the segmented characters to Tesseract OCR to do character recognition in the second model. The results show that our research can successfully recognize the license plate and its characters without fully satisfying the above two points and using high-end hardware equipment. | en_US |