外文文献翻译
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文献出处：Gao Q, Wang X, Xie G. Licen Plate Recognition Bad On Prior Knowledge[C]// IEEE International Conference on Automation and Logistics. IEEE, 2007:2964-2968.
英文原文
Licen Plate Recognition Bad On Prior Knowledge
Qian Gao, Xinnian Wang and Gongfu Xie
Abstract - In this paper, a new algorithm bad on improved BP (back propagation) neural network for Chine vehicle licen plate recognition (LPR) is described. The propod approach provides a solution for the vehicle licen plates (VLP) which were degraded verely. What it remarkably differs from the traditional methods is the application of prior knowledge of licen plate to the procedure of location, gmentation and recognition. Color collocation is ud to locate the licen plate in the image. Dimensions of each character are constant, which is ud to gment the character of VLPs. The Layout of the Chine VLP is an important feature, which is ud to construct a classifier for recog
nizing. The experimental results show that the improved algorithm is effective under the condition that the licen plates were degraded verely.
Index Terms - Licen plate recognition, prior knowledge, vehicle
licen plates, neural network.
I. INTRODUCTION
V ehicle Licen-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 curity [1]. VLP recognition system consists of the plate location, the characters gmentation, and the characters recognition. The 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. Becau this problem is usually ud in real-time systems, it requires not only accuracy but also fast processing. Most existing VLP recognition methods [2], [3], [4], [5] reduce the complexity and increa the recognition rate by using some specific features of local VLPs and establishing some constrains on the position, distance from the camera to vehicles, and the inclined angles. In addition, neural network was ud to increa the recognition rate [6], [7] but the traditiona
l recognition methods ldom consider the prior knowledge of the local VLPs. In this paper, we propod a new improved learning method of BP algorithm bad on specific features of Chine VLPs. The propod algorithm overcomes the low speed convergence of BP neural network [8] and remarkable increas the recognition rate especially under the condition that the licen plate images were degrade verely.
II. SPECIFIC FEA TURES OF CHINESE VLPS
门神的来历A. Dimensions环境布置图片
According to the guideline for vehicle inspection [9], all licen 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 licen plate have a fixed width, and the distance between the medium axes of two adjoining characters is fixed and the ratio between width and height is nearly constant. Tho features can be ud to locate the plate and gment the individual character. B. Color collocation of the plate
农业合作化运动There are four kinds of color collocation for the Chine vehicle licen plate .The color collocations are shown in table I.
TABLE I
Moreover, military vehicle and police wagon plates contain a red character which belongs to a specific character t. This feature can be ud to improve the recognition rate.
C. Layout of the Chine VLPS
The criterion of the vehicle licen plate defines the characters layout of Chine licen plate. All standard licen plates contain Chine characters, numbers and letters which are shown in Fig.1. The first one is a Chine character which is an abbreviation of Chine
provinces. The cond one is a letter ranging from A to Z except the letter I. The third and fourth ones are letters or numbers. The fifth to venth ones are numbers ranging from 0 to 9 only. However the first or the venth ones may be red characters in special plates (as shown in Fig.1). After gmentation process the individual character is extracted. Taking advantage of the layout and color collocation prior knowledge, the individual character will enter one of the class: abbreviations of Chine provinces t, letters t, letters or numbers t, number t, special characters t.
(a)Typical layout
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(b) Special character
Fig.1 The layout of the Chine licen plate
III. THE PROPOSED ALGORITHM
This algorithm consists of four modules: VLP location, character gmentation, character classification and character recognition. The main steps of the flowchart of LPR system are shown in Fig. 2.
Firstly the licen plate is located in an input image and characters are gmented. 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 reprents.
A. Preprocessing the licen plate
1) VLP Location
关于勤学的故事This process sufficiently utilizes the color feature such as color collocation, color centers and distribution in the plate region, which are described in ction II. The color features can be ud to
eliminate the disturbance o f the fake plate ’ s regions. The flowchart of the plate location is shown in Fig. 3.
Fig.3 The flowchart of the plate location algorithm
The regions which structure and texture similar to the vehicle plate are extracted. The process is described as followed:
Here, the Gaussian variance is t to be less than W/3 (W is the character stroke width), so 1P gets its maximum value M at the center of the stroke. After convolution, binarization is performed according to a threshold which equals T * M (T<0.5). Median filter is ud to prerve the edge gradient and eliminate isolated noi of the binary image. An N * N rectangle median filter is t, and N reprents the odd integer mostly clo to W.
Morphology closing operation can be ud to extract the candidate region. The confidence degree of candidate region for being a licen plate is verified according to the aspect ratio and areas. Here, the aspect ratio is t between 1.5 and 4 for the reason of inclination. The prior knowledge of color collocation is ud to locate plate region exactly. The locating process of the licen plate is shown in Fig. 4.企业经营管理
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2) Character gmentation
