Chinese License Plate Recognition System Based on Convolutional Neural Network

Abstract

License Plate Recognition (LPR) technology has been developed for many years, but for the time being, LPR technology still has problems such as not being accurate enough in positioning and too long recognition time. Especially in China, the License Plate (LP) is made up of Chinese characters, alphabets, and numbers, in which the use of Chinese characters profoundly influences the accuracy of LPR. In this paper, a Convolutional Neural Network (CNN) is employed in LPR system, and the LPR system designed in this paper includes three parts: coarse LP positioning, precise LP positioning, and LP character recognition. The LPR system designed in this paper uses the functions in OpenCV to call the laptop camera and input the obtained images into the computer to achieve interframe recognition by setting the timer. Once the image is acquired, the approximate license plate position is determined by the cascade classifier. After this, the image is processed by adaptive binarization, followed by Connected Component Analysis (CCA), linear fitting, and left-right regression to precisely locate the LP. After the exact location of the LP has been determined, it will be fed into a CNN for character recognition. Simulation tests show that the LPR system implemented in this paper has some LPR capability.