基于BP神经网络的车型识别-外文翻译.doc

一、外 文 资 料

License Plate Recognition Based On Prior Knowledge

Abstract - In this paper, a new 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 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

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 location, 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

systems, 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, 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

According to the guideline 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 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. Those

features can be used to locate the plate and segment the individual character.

B. Color collocation of the plate

There are four kinds of color collocation for the Chinese vehicle license plate .These color

collocations are shown in table I.

TABLE I

a specific character set. This feature can be used to improve the recognition rate.

C. Layout of the Chinese VLPS

The 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.1. The first one is a Chinese character which is an abbreviation of Chinese

provinces. The second 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 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.1).

After segmentation process the individual character is extracted. Taking advantage of the layout

and color collocation prior knowledge, the individual character will enter one of the classes:

(a)Typical layout

classification and character recognition. The main steps of the flowchart of LPR system are

shown in Fig. 2.

Firstly the license plate 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.

Number

segmentation

classifier

Chinese

character

Letter Letter or

number

Number Special character

Characters recognition

A. Preprocessing the license plate

1) VLP Location

This process 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

(1)

so 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 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 mostly 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

knowledge of color collocation is used to locate plate region exactly. The locating process of the