Abstract
License plate recognition systems are widely used in modern smart cities, such as toll payment systems, parking fee payment systems and residential access control. Such electronic systems are not only convenient for people's daily life, but also provide safe and efficient services for managers. License plate recognition algorithm is a mature but imperfect technology. The traditional location recognition algorithm is easily affected by light, shadow, background complexity or other factors, resulting in the failure to meet the application of real scenes. With the development of deep learning, the license plate recognition algorithm can extract deeper features, thus greatly improving the detection and recognition accuracy. Therefore, this paper discusses the application of deep learning in license plate recognition, and the main work is as follows: 1) Introduce the most advanced algorithms from the three main technical difficulties: license plate skew, image noise and license plate blur; 2) According to the process, the deep learning algorithms are classified into direct detection algorithms and indirect detection algorithms, and the advantages and disadvantages of the current license plate detection algorithms and character recognition algorithms are analyzed; 3) The differences in data sets, workstation, accuracy and time of different license plate recognition systems are compared; 4) Compare and illustrate the existing public license plate datasets according to the number of pictures, resolution and environmental complexity, and make a prospect for the future research direction of license plate recognition.
Highlights
With the rapid development of economy around the world, some cities in different countries may faced with traffic congestion, frequent accidents, traffic environment deteriorating or other urban traffic problems
License plate recognition system has been widely used in vehicle access management, expressway toll management, intelligent parking, electronic police and other aspects, which plays an important role in the supervision of vehicles, and can realize the supervision of urban traffic to prevent traffic jams, has important significance in real life
SUMMARY AND FUTURE DIRECTIONS The process of license plate recognition is usually divided into three steps, that is, license plate detection, character segmentation and character recognition
Summary
With the rapid development of economy around the world, some cities in different countries may faced with traffic congestion, frequent accidents, traffic environment deteriorating or other urban traffic problems. The factors that affect the accuracy of license plate recognition include illumination, shielding, the shooting angle, the shooting distance, camera resolution, background complexity and noise and so on According to these factors, license plate preprocessing is divided into three aspects, that is, tilt correction, image denoising and improving resolution, so as to obtain the ideal image after recovery. According to the literature statistics, the algorithms to improve license plate recognition results by image denoising are significantly less than those to improve tilt correction and resolution, which takes into account the tradeoff between accuracy and time. Traditional license plate location algorithms can be divided into five categories based on the intuitive features: text-based detection, color-based detection, character-based detection, and connected-component detection These intuitive features are affected by environmental, while deep learning can extract deeper features by pixel information. The above indirect detection algorithms are all composed of two different networks, which can tolerate a certain degree of light variation, distortion and blur
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