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英文原文及中文翻译

（一）英文原文

One: A Method of Slant Correction of Vehicle License Plate

Based on Watershed Algorithm

In a vehicle license plate recognition system, slant vehicle license plate has a bad

effect on the character segmentation and recognition. A method of slant correction of

vehicle license plate is proposed in this paper. The method consists of five main

stages: (1) the extraction of the boundaries of characters using watershed algorithm;

(2) dividing the boundaries of vehicle license plate into small segments using vertical

differential method; (3) connection of the fracture characters using expansion and

corrosion; (4) computing centroids of the left and the right part in the vehicle license

plate respectively; (5) finding the slant angle by means of two centroids. Experimental

results show that the error rate of using the method is 6.13%, which is lower than that

of the principal component analysis. The running time of using this method is less

than that of Hough transform. The method improves accuracy of the slant correction.

With the rapid development of highways and the wide use of vehicles, people have

started to pay more and more attention on vehicle license plate recognition system.

Vehicle license positioning, extraction and character segmentation are one of the most

difficult topics in the vehicle license plate recognition system. Slant vehicle license

plate has a bad effect on the character segmentation and recognition. In the last few

years some achievements in vehicle license positioning and slant correction have been

obtained. These achievements have distinguished effects in special conditions.

However, under a complex background, the effect of slant correction needs to be

enhanced further. Many problems such as: small contrast, non-uniform illumination,

image distortion as well as the contaminate dlicense plate and so on may bring

difficulty in slant correction of vehicle license plate. This article presents a method

(called SCWA method) of slant correction of vehicle license plate based on watershed

algorithm. As documented in the experiments of 460 vehicle license plates, the error

rate of using the SCWA method is 6.13%, which is lower than that of the principal

component analysis. The running time of using SCWA method is less than that of

Hough transform. Good slant correction is achieved with SCWA method. The paper is

outlined as follows: section I presents the introduction, section II describes the SCWA

method and section III presents a conclusion of the experiments of 460 vehicle license

images.

II. SCWA METHOD

A. Extraction of the Boundaries of Characters Using

Watershed Algorithm There are many boundaries of characters in the vehicle 2

license plate. These characters are very important to slant correction of vehicle license

plate. The steps of extraction of the boundaries of characters are as follow:1) Produce

gradient image The watershed algorithm is sensitive to noise and has excessive

segmentation. In order to avoid these problems, we apply prewitt operator to produce

gradient image of vehicle license. The prewitt operator is as follow:

where H1 is x direction border, H2 is y direction border, gradient magnitude is:

Watershed segmentation on gradient image

The gradient magnitude of the gradient image of the vehicle license plate is

considered as a topographic surface for the watershed transformation. The idea of

watershed segmentation can be viewed as a landscape immersed in a lake; catchment

basins will be filled up with water starting at each local minimum. Dams must be built

in order to avoid the merging of catchment basins. The water shed lines are defined by

the catchment basins divided by the dam at the highest level. As a result, watershed

lines can separate individual catchment basins in the landscape. The result of

watershed segmentation is shown in Figure 1. The watershed segmentation is as

follow: Assume that G is a simple connected graph, the distance between pixel x and

pixel y in G graph is the minimal route from pixel x to pixel y, min ( ) h I refers to

minimal gradient magnitude in license image I when the altitude is h, hmin and hmax

denote minimum and maximum in gradient magnitude domain DI respectively, h

value changes from hmin to hmax.

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Watershed segmentation orders gradient magnitudes according to increase and then

scans from hmin to hmax according to width preferential algorithm.

Step 1. These pixels whose gradient magnitude is h are marked with a flag sign. The

pixels which are marked with a flag sign are put into first-in-first-out queue.

Step 2. A pixel P is got from the queue. Assume that P’ around pixel P is the same

flag region as P. P’ and P are merged if the distance between P’ and P is smaller than

the current distance.

Step 3. P' is put into first-in-first-out queue if the distance between P' and the marked

regions is not computed. P' distance is that the current distance adds 1.

Step 4. The current distance adds 1 when the computation of current distance has

finished.

Step 5. Go to step 2 if the queue is not empty.

Step 6. Sign a new mark for these pixels which are not handled from step 2 to step 4

and which are min ( ) h I .

B. Dividing the Boundaries of Vehicle License Plate into Small Segments Using

Vertical Differential Method Respecting the more intensive density of the vertical