车牌识别外文文献翻译中英文.docx

1、外文文献翻译(含：英文原文及中文译文)文献出处：Gao Q, Wang X, Xie G. License Plate Recognition Based On Prior KnowledgeC/ IEEE International Conference on Automation and Logistics. IEEE, 2007:2964-2968.英文原文License Plate Recognition Based On Prior KnowledgeQian Gao, Xinnian Wang and Gongfu XieAbstract - In this paper, a ne

2、w algorithm based on improved BP (back propagation) neural network for Chinese vehicle license plate recognition (LPR) is described. The proposed approach provides a solution for the vehicle license plates (VLP) which were degraded severely. What it remarkably differs from the traditional methods is

3、 the application of prior knowledge of license plate to the procedure of location, segmentation and recognition. Color collocation is used to locate the license plate in the image. Dimensions of each character are constant, which is used to segment the character of VLPs. The Layout of the Chinese VL

4、P is an important feature, which is used to construct a classifier for recognizing. The experimental results show that the improved algorithm is effective under the condition that the license plates were degraded severely.Index Terms - License plate recognition, prior knowledge, vehiclelicense plate

5、s, neural network.I.INTRODUCTIONVehicle License-Plate (VLP) recognition is a very interesting but difficult problem. It is important in a number of applications such as weight-and-speed-limit, red traffic infringement, road surveys and park security 1. VLP recognition system consists of the plate lo

6、cation, the characters segmentation, and the characters recognition. These tasks become more sophisticated when dealing with plate images taken in various inclined angles or under various lighting, weather condition and cleanliness of the plate. Because this problem is usually used in real-time syst

7、ems, it requires not only accuracy but also fast processing. Most existing VLP recognition methods 2, 3, 4, 5 reduce the complexity and increase the recognition rate by using some specific features of local VLPs and establishing some constrains on the position, distance from the camera to vehicles,

8、and the inclined angles. In addition, neural network was used to increase the recognition rate 6, 7 but the traditional recognition methods seldom consider the prior knowledge of the local VLPs. In this paper, we proposed a new improved learning method of BP algorithm based on specific features of C

9、hinese VLPs. The proposed algorithm overcomes the low speed convergence of BP neural network 8 and remarkable increases the recognition rate especially under the condition that the license plate images were degrade severely.II.SPECIFIC FEATURES OF CHINESE VLPSA. DimensionsAccording to the guideline

10、for vehicle inspection 9, all license plates must be rectangular and have the dimensions and have all 7 characters written in a single line. Under practical environments, the distance from the camera to vehicles and the inclined angles are constant, so all characters of the license plate have a fixe

11、d width, and the distance between the medium axes of two adjoining characters is fixed and the ratio between width and height is nearly constant. Those features can be used to locate the plate and segment the individual character. B. Color collocation of the plateThere are four kinds of color colloc

12、ation for the Chinese vehicle license plate .These color collocations are shown in table I.TABLE IMoreover, military vehicle and police wagon plates contain a red character which belongs to a specific character set. This feature can be used to improve the recognition rate.C. Layout of the Chinese VL

13、PSThe criterion of the vehicle license plate defines the characters layout of Chinese license plate. All standard license plates contain Chinese characters, numbers and letters which are shown in Fig.l. The first one is a Chinese character which is an abbreviation of Chineseprovinces. The second one

14、 is a letter ranging from A to Z except the letter I. The third and fourth ones are letters or numbers. The fifth to seventh ones are numbers ranging from 0 to 9 only. However the first or the seventh ones may be red characters in special plates (as shown in Fig.l). After segmentation process the in

15、dividual character is extracted. Taking advantage of the layout and color collocation prior knowledge, the individual character will enter one of the classes: abbreviations of Chinese provinces set, letters set, letters or numbers set, number set, special characters set.(a)Typical layout(b)Special c

16、haracterFig.l The layout of the Chinese license plateIII.THE PROPOSED ALGORITHMThis algorithm consists of four modules: VLP location, character segmentation, character classification and character recognition. The main steps of the flowchart of LPR system are shown in Fig. 2.Firstly the license plat

17、e is located in an input image and characters are segmented. Then every individual character image enters the classifier to decide which class it belongs to, and finally the BP network decides which character the character image represents.A. Preprocessing the license plate1) VLP LocationThis proces

18、s sufficiently utilizes the color feature such as color collocation, color centers and distribution in the plate region, which are described in section II. These color features can be used to eliminate the disturbance of the fake plate s regions. The flowchart of the plate location is shown in Fig.

19、3.Fig.3 The flowchart of the plate location algorithmThe regions which structure and texture similar to the vehicle plate are extracted. The process is described as followed:Here, the Gaussian variance is set to be less than W/3 (W is the character stroke width), so IP gets its maximum value M at th

20、e center of the stroke. After convolution, binarization is performed according to a threshold which equals T * M (T0.5), Median filter is used to preserve the edge gradient and eliminate isolated noise of the binary image. An N * N rectangle median filter is set, and N represents the odd integer mos

21、tly close to W.Morphology closing operation can be used to extract the candidate region. The confidence degree of candidate region for being a license plate is verified according to the aspect ratio and areas. Here, the aspect ratio is set between 1.5 and 4 for the reason of inclination. The prior k

22、nowledge of color collocation is used to locate plate region exactly. The locating process of the license plate is shown in Fig. 4.2) Character segmentationThis part presents an algorithm for character segmentation based on prior knowledge, using character width, fixed number of characters, the rati

23、o of height to width of a character, and so on. The flowchart of the character segmentation is shown in Fig. 5.Firstly, preprocess the license the plate image, such as uneven illumination correction, contrast enhancement, incline correction and edge enhancement operations; secondly, eliminating spac

24、e mark which appears between the second character and the third character; thirdly, merging the segmented fragments of the characters. In China, all standard license plates contain only 7 characters (see Fig. 1). If the number of segmented characters is larger than seven, the merging process must be

25、 performed. Table II shows the merging process. Finally, extracting the individual character image based on the number and the width of the character. Fig. 6 shows the segmentation results, (a) The incline and broken plate image, (b) the incline and distort plate image, (c)the serious fade plate ima

26、ge, (d) the smut license plate image.where Nf is the number of character segments, MaxF is the number of the license plate, and i is the index of each character segment.The medium point of each segmented character is determined by:(3)where li Sis the initial coordinates for the character segment, an

27、d 2i S is thefinal coordinate for the character segment. The distance between two consecutive medium points is calculated by:(4)Fig.6 The segmentation resultsB. Using specific prior knowledge for recognitionThe layout of the Chinese VLP is an important feature (as described in the section II), which

28、 can be used to construct a classifier for recognizing. The recognizing procedure adopted conjugate gradient descent fast learning method, which is an improved learning method of BP neural network10. Conjugate gradient descent, which employs a series of line searches in weight or parameter space. On

29、e picks the first descent direction and moves along that direction until the minimum in error is reached. The second descent direction is then computed: this direction the conjugate direction is the one along which the gradient does not change its direction will not spoil the contribution from the p

30、revious descent iterations. This algorithm adopted topology 625-35-N as shown in Fig. 7. The size of input value is 625 (25*25 ) and initial weights are with random values, desired output values have the same feature with the input values.As Fig. 7 shows, there is a three-layer network which contain

31、s working signal feed forward operation and reverse propagation of error processes. The target parameter is t and the length of network outputvectors is n. Sigmoid is the nonlinear transfer function, weights are initialized with random values, and changed in a direction that will reduce the errors.T

32、he algorithm was trained with 1000 images of different background and illumination most of which were degrade severely. After preprocessing process, the individual characters are stored. All characters used for training and testing have the same size (25*25 ).The integrated process for license plate recognition consists of the following steps:1)Feature extractingThe feature vectors from separated character images have direct effects on the recognition rate. Many methods can be used to extract feature of the image samples, e.g. statistics of data at vertica