As technology advances, the need of image recognition becomes important as well. License plate recognition is one of the common applications for image recognition. Identifying license plate numbers has many applications in life, such as parking fees, traffic violations, etc., thus improving the accuracy of license plate recognition can improve convenience in life, reduce capital expenditure, and improve law enforcement efficiency. This paper compares the effects of different classification methods for identifying license plate numbers, and hopes to establish a license plate recognition system. Since license plate images come from different regions, we increase the training samples by a data augmentation method to improve the recognition accuracy. License plate recognition can be divided into three stages: detecting the license plate area, character segmentation of license plate, and identifying the license plate number. This article explores the feasible methods at each stage respectively, using pixel intensity and histogram of oriented gradients (HOG) as the feature vector to build the models. We also use indicator to evaluate which model is best for license plate recognition and explore the identification of each number and letter.