CHINESE LICENSE PLATE RECOGNITION BASED ON HUMAN VISION ATTENTION MECHANISM

Abstract

License plate recognition (LPR) is one of the most important elements affecting intelligent transportation systems. A number of LPR techniques have been proposed. Humans are good target recognition systems. In other words, humans easily recognize common objects. In this paper, the researchers present a novel method of recognizing Chinese license plates. The method is based on the Human Vision Attention Mechanism (HVAM) and uses Chinese license plates as the targets. The research consists of three stages. The first stage involved finding and identifying license plates in videos of moving vehicles. The second stage separated each license plate into the seven characters. In the third stage, the character recognizer extracted some salient features of Chinese characters and used a multi-stage classifier to recognize each character on the license plate. In the experiment locating license plates, 1176 images taken from various scenes and conditions were employed. The method failed to identify the license plates in only 27 of the images; resulting in a license plate location rate of success of 97.7%. In the experiment for identifying license characters, 1149 images were used, from which license plates had been successfully located. The method failed to identify the characters in 45 of these images giving a success rate of 96.1%. Combining the above two rates, the overall rate of success for our LPR is 93.9%.