基于BP神经网络的车牌识别技术研究(英文版).doc

Research on license plate recognition technology based on BP neural networkWith the continuous development of science and technology, means of traffic management is from manual management gradually transformed into automatically or semi automatically, license plate recognition as one of the key and hot issues in the research field of modern traffic engineering by more and more peoples attention. In recent years, neural networks have been applied in many fields, and the characteristics of neural networks are used to make the character recognition based on BP neural network.This article through to in license plate recognition system image pre processing, four key steps: license plate location, character segmentation and character recognition of proposed a kind of license plate characters based on neural network recognition algorithm. Used this method of license plate image experiments were conducted to extract the feature of the license plate character sample, and under the environment of MATLAB on the license plate character recognition was simulated. The results showed that this algorithm the characters on the license plate location and segmentation has good effect, the license plate character recognition with certain accuracy.Key words: BP neural network; license plate location; license plate recognition; character segmentation; character recognition1 IntroductionWith the increase of the number of cars, there are traffic congestion in the world. In order to solve this problem, many cities will be widened lane, but still far from solving the problem. Not to increase the existing road facilities, how to improve the efficiency of transportation has become the focus of research in the world. Intelligent transportation system (Intelligent - Transportation System ITS) is the main development trend of the future traffic regulation system. Vehicle license plate recognition technology (License - Plate Recognition LPR) is one of the core technologies in ITS. Therefore, the research and development of license plate recognition system is of great practical value for the development of Chinas traffic management field.At present, there are still many problems in the license plate recognition system. Recognition rate is not possible to do one hundred percent, but with the deepening of research, license plate recognition technology will gradually mature. The development of modern intelligent transportation, make it has great potential for application, a broader market. At the same time, neural network in classification problems get widely used, for license plate recognition problem, we must first find the license plate features, and corresponding evaluation data, using these data to train neural network.Because the artificial neural network has the characteristics of parallel processing, distributed storage and fault tolerance, it is widely used in the LPR system. The parallelism of the structure makes the information storage of the neural network adopt the distributed mode, that is, the license plate character information is not stored in a part of the network, but is distributed in the network of all the connections. These features are bound to make the neural network in the license plate recognition of the two aspects of the performance of a good fault tolerance:(1) because of the distributed storage of the character characteristic information, the whole performance of the vehicle license plate recognition system will not be affected when some of the neurons in the network are damaged.(2) neural network through pre stored information and learning mechanisms for adaptive training, can never complete license plate information and noise of the license plate image by Lenovo to restore full memories of the original, in order to achieve the correct identification of the incomplete input information.Based on the above characteristics, the application of artificial neural network in the vehicle license plate recognition system has great research value.2 introduction the principle of BP neural networkBP (back propagation) network is proposed the scientists group 1986 by Rumelhart and McCelland headed, is a kind of error inverse propagation training algorithm for the multilayer feedforward network and is currently the most widely used models of neural network. BP network can learn and store a lot of input - output model mapping, without prior mathematics describing this mapping equation. Its learning rule is the steepest descent method is used to adjust the weights and thresholds of the network through the back-propagation network, the minimum error sum of squares. BP neural network topology, including input layer, hidden layer (input) (hide layer) and output layer (output layer).2.1 BP algorithmThe error back-propagation algorithm (BP algorithm) of the learning process, by the reverse forward propagation and error information transmission consists of two processes. Input layer neurons receives the input information from the outside world, and passed to the middle layer neurons; intermediate layer is internal information processing layer and is responsible for the information transform, according to the demand of the information changes, the middle layer can be designed for single hidden layer or multi hidden layer structure; the last hidden layer transfer to output layer neurons, after further processing, to complete a learning forward propagation process, from the output layer output to the outside information processing results. When the actual output is not in conformity with the expected output, the reverse propagation phase of the error is entered. The error is corrected by the output layer, and the weight of each layer is corrected by the error gradient descent method. The cycle of information forward propagation and error back propagation process, the constant adjustment of the weights of each layer, is the learning and training of neural network process, this process has been carried out to network output error reduced to an acceptable level, or pre-set learning times so far.3 license plate recognition principleA complete vehicle license plate recognition system is divided into the following four steps: 4. As shown below:车牌定位字符分割图像处理车牌识别识别训练1) image processing:No matter on the improvement of the license plate image identifiable degree, or simplified location and segmentation of the characters, image conversion and data compression, image correction and image enhancement processing is very necessary.(2) license plate location:Mainly including the edge of the license plate image extraction and two values, the license plate level direction of the positioning algorithm, the vertical direction of the license plate location algorithm. Finally determine the relative position of the license plate in the entire image, the output of the rectangular license plate image.(3) character segmentation:A single character is obtained by using the character location and segmentation method, which is used to detect the number of pixels.(4) character recognition:The template matching method is used to match the characters in the neural network database to confirm the character, get the final license, including the English letters and numbers.4 system design and ImplementationThe establishment of 4.1 BP neural networkBP network is is applied very widely used as a feedforward neural network, is similar to the human brain and high degree of parallelism. Good fault tolerance and associative memory function, adaptive learning and fault tolerance ability are strong, from the theoretical research shows, with a single hidden layer neural network enough to perform arbitrarily complex function mapping system. Therefore, we choose the has a hidden layer of three layer BP neural network to realize the character recognition. With artificial neural network character recognition mainly has two kinds of methods: one method is to treat the character recognition feature extraction, and then to train the neural network classifier with the feature. The extraction and recognition effect of character features, and character feature extraction is often time-consuming. Therefore, the character feature extraction becomes the key research. The other way is to make full use of the characteristics of the neural network, directly to the processing of image input network, automatically by the network to realize the feature extraction and recognition. Here, I used second methods to identify the character.与模板样版进行计算寻找相关度最大的模块读入字符根据模块输出值The neural network is composed of two stages:(1) learning period:The connection weights between neurons can be modified by learning rules in order to minimize the objective function.Vehicle license plate characters seven, most license plate first Chinese characters, usually represent the vehicle belongs to the provinces, or is services, police dont have referred to as the specific meaning of the characters, followed by the letters and numbers. License plate character recognition and general character recognition is that it has a limited number of characters, a total of about more than 50 Chinese characters, 26 English letters, numbers 10. So it is very convenient to set up the character template library.The license plate recognition of Chinese characters, letters and numbers, but the number is not very large, but for Chinese characters, there is only a Su. Letters and numbers are the numbers and letters set up by the CS PHOTOSHOP process, which are collected from the Internet, and used to build the template library.Chinese characters included in the library are: Beijing, Zhejiang, Jiangsu, Henan, Henan, Shaanxi, Shaanxi, Lu, letters are: A-Z, the number of libraries are: 0-9.(2) working period:In this paper, the number of hidden layer neurons is 13, and the number of output neurons is 6 BP neural network is trained.The connection weights are constant, and the corresponding output is obtained from the input of the network. After reading the training samples according to the program for neural network training. Using BP neural network training, hidden layer of 13 neurons, the output layer of 6 neurons, the training step 2000, the expected error of 0.0001, the learning rate of 0.0003.The training process is shown below:4.2 license plate locationA single traditional license plate location method, it is difficult to achieve satisfactory results in practical applications. So we need a new license plate location method to explore a variety of methods, in order to practical application, to achieve the desired effect. This is a kind of color features and texture features combined with the license plate location method.Will automobile image into the computer, image pretreatment, and according to the color feature of license plate image and combined with prior knowledge of the license plate dimensions find the exact location of license plate is located in the vehicle image, and segment the license plate frame, accurate car brand image. As shown in Figure 4.3 of the license plate location.1) pretreatment:After getting the vehicle image, the image is first processed. The work is to adjust the brightness and contrast of the image, to prepare for the following work.(2) grayscale:Transform the image into a grayscale image. After getting the gray map, advanced gray stretch, in order to improve the image contrast, for the next step to prepare for the gray jump.(3) rough positioning:Use of the characteristics of the license plate, that is, the foreground and background of the license plate area has a strong contrast and contrast effect, in the gray image in the character and background edge of the gray value of a large jump (gray value change in more than 50). It is found that the intensity of the gray level of the license plate is significantly higher than that of the non license plate region when the level of the gray level image is scanned by the experiment. Using this feature, you can roughly determine the level of the license plate area, thus narrowing the search range. Thus can safely complete the rough positioning of the license plate. The specific steps of the upper and lower boundary localization are:From top to bottom line by line scanning;In order to the highest level of the behavior of the highest level of the behavior of the benchmark, up and down to scan;If the gray jump is less than a certain threshold, then stop scanning. Where the upper and lower, that is, the upper and lower boundaries of the license plate.After the license plate region is located on the lower boundary, it is necessary to make the left and right positioning. The method of the right and left position is similar to the method of up and down position:After the upper and lower positioning of the gray map, from left to right column by column scanning;With the highest intensity of the column as the benchmark, the left and right of the scanning;If the intensity of the gray jump is greater than a certain threshold, then stop scanning. The left and right columns, which are the left and right boundary of the license plate.(4) accurate positioning:In this paper, we use the theory of HSI color space 5. HSI color space is similar to the uniform color space, that is, HSI color space of two points in the Euclidean distance and the degree of human perception is directly proportional to the RGB space, it is more in line with human intuition. The chro