車牌識(shí)別系統(tǒng)外文文獻(xiàn)

1、Vehicle License Plate Recognition System Based on Digital Image Processing Yao Yuan,Wu xiao-li Department of Computer Science and Engineering, Henan University of Urban Construction eyaoyuan@126.com Akf1l'lcl-This pa

2、per analyzes the basic method of digital video image processing, studies the vehicle license plate recognition system based on image processing in intelligent transport system, presents a character recognition approach b

3、ased on neural network perceptron to solve the vehicle license plate recognition in real-time traffic flow. Experimental results show that the approach can achieve better positioning effect, has a certain robustness and

4、timeliness. Keywonls-veltic/e license plate recognition; imllge processing,· “igill /ID'fJltology I. INTRODUCTION Since the 21st century, with social development and improvement of living standards, the nu

5、mber of vehicles is continuously increased, the traffic conditions is worsening, which brought huge pressures to the society and environment. Intelligent transport system is a real-time, accurate, and efficient tr

6、ansportation management system built based on a relatively perfect road infrastructure and by synthetically using the advanced electronic technology, information technology, sensor technology and systemic engineering te

7、chnology in ground transportation [1] . d management . This system can solve the vanous roa problems generated by the traffic congestion, thus receiving more and more attention. Vehicle license plate recognition is

8、 one of the key technologies in the intelligent transport system, while its development is rapid, has been gradually integrated into our real life. Vehicle license plate recognition system can carry out automatic registr

9、ation, verification, monitoring and alarm management, is an important part of modem highway toll management system, highway speed automatic monitoring system, highway surveillance, parking automatic charging manag

10、ement and other fields. II. VEHICLE LICENSE PLATE RECOGNITION SYSTEM A. Vehicle license plate recognition system overview Vehicle license plate recognition system is mainly composed by hardware and software. The hardw

11、are part includes a control computer, one Ethernet camera, a UPS power supply and an interface control port. These sections ensure the car images intake and processing. The software part is divided into the Ethernet came

12、ra embedded front-end software and the processing software in the industrial computer. Vehicle license plate recognition system usually consists of data acquisition (license plate image acquisition), license plate e

13、xtraction, and license plate identification several major components, the system architecture as shown 978-1-4244-5540-9/10/$26.00 ©2010 IEEE 667 in figure 1. I Image extraction Recognition results ? Image prepro

14、cessing Information recognition Figure I. Vehicle license plate recognition system structure In the vehicle license plate recognition system, the image acquisition is completed mainly by the hardware, which is to extrac

15、t the foreground image of the vehicle, to convert the camera's video signal to digital image signals to be sent to the computer for processing. Because the impact of the natural environment and the lighting condition

16、s, there are many disturbances in the license plate images, which brings inconvenience to the positioning of the license plate, so in order to better extract the license plates, it needs to preprocess the license plate i

17、mage to ensure the license plate location quality. VLP detection, this part is the core of the system, and the implementation of which affects the performance of the whole system, which is mainly to use pattern reco

18、gnition [2] , digital image processing, information theory and other knowledge to position and extract the license plate in the license plate images. Character segmentation and recognition, when the plate has been

19、 successfully extracted, it needs to segment the characters in which, and use prior knowledge to identify them to get the final results. B. Key technologies o/license plate recognition 1) Vehicle license regional posi

20、tioning technology: it is to use the above characteristics to determine the true location of license plate. To accurately position the vehicle license plate .from the images obtained .from the natural scene is the ke

21、y of the vehicle license plate recognition system, is also the most dificult ste p. 2) Vehicle license plate character segmentation technology: it is to divide the license plate region into a single character regio

22、n .for the follow-u p license plate recognition module to ident!fY the single characters. J) Vehicle license plate character recognition technology: character recognition is the process of effictive confirming t

23、he Chinese characters, English letters and numbers on the license plate on the basis of the accurate l“J segmentationfor the vehicle license plate character . Features information match '!_----, END Figure 3. Sys

24、tem specific processing flow chart Based on the hundreds of pieces of the vehicle license images this paper carries out a license positioning and segmentation test, the results show that the correct rate can reach 96%. T

25、he automatic segmentation results can meet the requirements of character segmentation and recognition; and the recognition range is accurate, the area size is appropriate, there is no missed part of the license [3] ; for

26、 the image without ideal light conditions, an image enhancement can be carried out once to make the dynamic range of image gray expanded and the contrast enhanced, and then for the image positioning and segmentation, thu

27、s, to improve the accuracy of image segmentation. B. System peJjOrmance analysis 1) Accuracy analysis In order to achieve the purpose of real-time processing, the algorithms used in this system are not involved with c

28、omplicated mathematical functions, and in such circumstances the system achieves good results, because the parts the system involved are more, so the output of each part can be the input of the next part. Linked tog

29、ether, the previous module error necessarily will lead to the latter modules error. Therefore, the system is a typical serial system, and the overall accuracy depends on the product of the various part accuracy. 2) D!lJ

30、iculty analysis In the image acquisition, the different object distances often result in different license plate sizes in the image. And the processing method of a fixed threshold adopted in the previous algorithm has no

31、t a universal adaptability. A fixed threshold can only handle a certain size of license plate images, but for other images with different sizes is helpless. Faced to a large number of license plate images with 669 differ

32、ent sizes, to find a new algorithm with wider applicability is not easy. In actual image acquisition, the noise, light has a great influence on the image quality. A lot of random noise disturbance, different pers

33、pectives of the light, light, resulting in license plate light and dark gray irregular changes. The irregular and uncertain occurrence of the deformation, noise and other interference information all make the clarity of

34、the captured license plate image greatly reduced. The difference of the angel when image collecting, the actual front license plate and the license plate incline will cause the captured license plate graphic to ge

35、nerate geometric deformation [5] . And the license plate graphic geometric deformation, the different degrees of the deformation, also make the license plate positioning in the license plate image and the lice

36、nse plate character recognition more difficult. This requires the license plate location and recognition to have high anti-interference and robustness. IV. CONCLUSION Vehicle license plate intelligence recognition sys

37、tem as the core of traffic identification system will play an important role in the future traffic control. This paper studied the vehicle license plate recognition system based on image processing in the intelligent tra

38、ffic system, proposed a character recognition solution based on neural network perceptron to solve the license plate recognition problem in the real-time traffic flow, and also had some research on the vehicle license pl

39、ate character recognition algorithm, the test results showed that the system had high anti-interference and robustness. REFERENCES (2). Dai Yan, Ma Hongqing, Liu Jilin. High Performance License Plate Recognition System B

40、ased on the Web Technique, IEEE Intelligent Transportation Systems Conference, August 25-29, 2001, 325-334. (3). Taleb A, Hamad A, Tilmant D. Vehicle license Plate recognition in marketing application [C]. IEEE Transact

41、ion on Intelligent Vehicles Symposium, 2003, 90-94. (4). Kawaguchi H. Application system using license Plate recognition technology [Jl. NEC Technical Journal, 2005, 54(7): 19-22. (5). Tsang-Hong Wang, Feng-Chou Ni, Ke

42、h-Tsong Li et al. Robust License Plate Recognition based on Dynamic Projection Warping. In: Sensing & Control, Taipei, Taiwan, 2004, 784-788. (6). Wenjing Jia, Huaifeng Zhang, Xiangjian He et al. Mean Shift for Acc

43、urate License Plate Localization. In: Intelligent Transportation Systems, Vienna, Austria, 2005, 566-570. (7). Hyun-Chul Kim, Shaoning Pang, Hong-Mo Je, Daijin Kim, Sung Yang Bang. Pattern Classification Using Support V