car license plates detection from complex scene

8/4/2019 Car License Plates Detection From Complex Scene

http://slidepdf.com/reader/full/car-license-plates-detection-from-complex-scene 1/6

Proceedingsof ICSP2000

Car License Plates Detection from Complex Scene

Da-shan Gao Jie Zhou

Department of Automation,TsinghuaUniversity

Beijing 100084,[email protected] zhoLi~ie(cc,infa.aii.tsinlrhua.edu.cii

Abstract: In this paper, we present a novel approach

to extract car license plate from complex image

without reading attempt. After an algorithm of

segmen tation, a series of ca ndidate regions are

obtained first. Then a confidence value based on the

geometrical features of license plates is given to eachcandidate region and merge operation under some

rules is taken. E xperim ental results show that thealgorithm is robust in dealing with different conditions

such as poor illumination and distortion of image

generated by different visual angle .

1. Introduction

Automatic recognition of car license plates plays a n

important role in traffic surveilla nce systems. Such

systems, which are applied in parking areas, highways,

bridges and tunnels, can help a human operator andimprove the overall quality of a service. Any situation

requiring the automatic control of the presence and

identification of a motor vehicle provided with a

license number may represent a potential application.

Recently, we have seen quite a few computer-vision-

based systems that recognize the license plates [l-91.

Most existing systems focus on the development of a

reliable optical character recognizer (OCR). Howe ver

prior to the recognition an OCR system performs, the

license plate has to be extracted from a variable of

scenes. Since there are problems such as poor am bient

lighting problem, visual a ngle, ima ge distortion and so

on, sometimes the car license plate is difficult to be

extracted.Many techniques have been reported in previous

researches. Hough Transform for line detection was

proposed in [3] on the assumption that the shape of

license plate is defined by lines. Com bining extraction

of license plates with character recognition by BP

neural networks was used in [4]. 5,6] used neural

networks (NN) with some features in car license plate

such as color and so on. Vector Quantizationmethodology and distributed genetic algorithm was

used in [7 ] an d [8], respectively. Although the

algorithm proposed in [9] is robust for recognition of

inclined license plates due to different visual angle, it

depends on the high quality acquired by a special

CCD and a set of strict prior know ledge.

In this paper we proposed a novel method to extractcar license plates from a complex scene by

considering both the distributive regulation of the

characters in a license plate and the geometrical

features of a license plate. In our approach, we first

present a segmenting algorithm, looking for thecandidate regions that probably contain charac ters in a

proper size. Then we give each candidate region aconfidence value to measure its likelihood to be a

license plate and combine these regions according to

some rules to get a higher confidence value. Then the

car license plate can be found to have highe st value.

2. Car license plate detection

2.1. Outline of the algor i thm

In this algorithm we present a technique for the

location and extraction of car license plates in

complex scenes. As schematized in Figure 1 the

algorithm works in four major steps: preprocessing,

extraction of candidate regions, morphological

processing, endowing confidence value and region

merge.

2.2. Preprocessing

As the image that contains car license plates are

acquired in a real environment under uncontrolled

0-7803-5747-7/00/$10.00Q2000 IEEE.

mailto:[email protected]

mailto:[email protected]



illumination, it often shows thick shade and low

contrast. In order to reduce the undesired effects and

enhance the contrast, histogram equalization for

contrast enhancem ent can be used when necessary.Histogram Equalization is a technique in image

processing, through which we desire to take a given

input image into an output image with equally manypixels at every gray level (a flat histogram) Ell]. In

practice, we set a threshold observed in a set ofimages and equalize the image whose accumulativehistogram is below this threshold. After histogramequalization a contrast enhancement is applied by a

sigmoid transform function,

f ( x ) = 1+exp(--a * ( x - d ) )

where c, d and a are constant which determine the

maximum value, center and shap e of the function.

C

(1)

2.3. Extraction of candidate regions

Considering the characters in a car license plate

always have a distinctive gray level to the background

of the license plate, which is to say that a car license

plate have a relative high contrast, we can get thefollowing features of a license plate in its gradientimage.

Firstly, the average gradient value of the region that

contains a license plate is high because of the intensevariations in it, as is mentioned in [I]. The size of thewindow where the local average gradient value is

calculated can be set to correspond to the size oflicense plates in the majority of images acquiredthrough a CCD camera.

Secondly, the variance of the gradient image of a

license plate region is relative low because there are anumber of edges of characters in it. So the variance is

calculated in a window of certain size to distinguish a

license plate region from a long edge with only highcontrast.

Dividing the average gradient value by the local

variance of gradient image at each pixel, we perform abinary method successively and get some candidateregions, which probably contain a car license.

2.4. Morphological Processing

After the image are segmented by thresholding,

there may be. some noise in the image such as isolated

dots, long vertical or horizontal stripes and so on . Soan opening operation of morphological processing[113, in which a Dilation operation is performed after

an erosion operation, is applied in order to reduce the

undesired effect of noise, smooth the edges of the

candidate regions and to separate the regions which

should be separated.

2.5.Geom etrical Criteria an d Confidence Value

To detect a license plate from a complex scene is a

kind of simplified detection of a text line in an imagein a sense [lo]. It is easier because of the fixed

geometrical structure of license plates, which appearsin almost the same shape of rectangle and contains

characters with the same number. So we specify some

geometrical criteria and confidence functions, thevalue of which is from 0 to 1, based on the internalfeatures of a license plate to depict the likelihood

between a candidate region and a license plate region.In the following, we discuss these internal features

respectively.Area. The area o f a region is defined as the number

of its pixels. As a recognizable license plate, it mustcontain quite a few pixels. So the larger the area of aregion is, the higher the confidence v alue will be.

Elongation. A license plate can be regard as ahorizontal rectangle with particular ratio of width andheight. Even though sometimes it is distorted in aimage from different visual angle, it still can be

bounded by a skew rectangle with approximate rationof width and height. With this prior knowledge, we

find the two axis of a region through K-L transformand make a minimum rectangle to bind the region.The elongation feature is defined as the ratio of widthand height of the rectangle. The more approximate to

the ratio of a real license plate region the elongation is,

the higher a confident value will be given.Density. It is defined as the ratio between the

region area and the area of a bounding rectanglediscussed above. In general, a license plate region is

fully filled. So the index permit to detect sparsely

filled regions, which is given a low confidenc e value.

Proximity to the image frame (PIF). Theproximity index is defined as the distance between the

pixels of the region and the image frame, normalized

with respect to the corresponding image size. In manycases of application in traffic control system, a car is

the focus in a image or almost is. So a license platecan .be found in relative center region of an image.

This feature is introduced to identify such noisy

regions that often appear along the border of theimage.



Gross Confidence Value. The gross confidence

value is defined as the sum of the confidence value of

each feature with w eighting factor, i.e.

p(r )= xa ic = f Area,Elongation,Density,PIF)

where c, s confidence value of feature i,ai s the

weighting coefficient.

I

2.6.Merge Rules.

Jh our algorithm of extracting candidate regions, a

car license plate is probably separated into several

adjacent regions, which are unlike the license plate

according to geometrical criteria respectively andmust have low confidence values. In order to make the

license region have the highest confidence value, a

merge operation is performed to incorporate these

separated regions together. The merge operation must

conform to the rules as follows:

Suppose rl and r2 are two regions and p(.) is

the gross confidence value of a region. rl and r2

can be incorporated into one region, r , if the

following rules is satisfied.

1) rl an d r2 is close to each other, i.e. the

distance between rl an d r2 is bounded in a

certain range;

2) max{p(rl>,p ( r 2 ) )5 p ( r ) 5 1

We applied such merging operation repeatedly

until there are no regions can be merged. Then thecandidate regions are sorted by the gross confidence

value. The first region, which has the largest confident

value, is regard as the license plate region. Figure.2

shows the images derived from each p rocedures of our

detection algorithm.

3. Some experimental results

We apply the above algorithm to our database of

car license plates, all of which are real scene images

acquired by CCD cameras. They contain cars in

different conditions, such as different illumination and

different visual angle. Figure.3 shows some test

images in our experiment.

Table. 1 shows the result of o ur experiment. From it

we can see that, in most cases the car license plates

can de detected effectively.

Our algorithm Tailed in 16% cases. The failures

were caused mainly by 3 reasons. First, the size of thelicense plate is beyond the maximum size

hypothesized. Secondly, a well-proportioned

illumination in the whole image but in the license

region there is a dark shade. So in this case Histogram

Equalization can be useless. The last reason is that

there are some signboards with the same geometrical

features with license plate.

In summary, the algorithm can be applied in acertain range of the size of license plate which is

according to the concrete situations. In differentsituations, we can adjust the size of window to

coincide with it.

The time spe nt to run the algorithm depends on th e

size of windows and the size of the image processed.

In the experiment we applied it on the PC with CPU

PIII 450 under window size 11*23, the time spent in

this algorithm is less than 2 seconds at image size

300*300.

Table.1 Resultof the license plate extraction algorithm

applied on a car database. The size of the window in

Section.2.3 is 11*23.

Total Detected as 1" Detected as Missing

images candidate 2"dcandidate

119 96 4 19

( % ) 80.7 3.36 16.0

4. Conclusions

In this paper, a novel algorithm of extracting car

license' plate in a comp lex image is proposed .

Considering the distribution of characters in a license

plate and the geometrical features of a license plate

comp rehensively, we a pply a set of confidence values

to candidate regions and combine them under some

rules. The algorithm only prior knowledge of the

range of license size, so it is robust to the deterioration

of the imag e such as blur. The a lgorithm is also robust

to detect the distorted license plate derived from

different visual angles because we applied a skew

rectangle generated by a K- L transform to bind the

license region.

The algorithm can d etect different size of license to

some extent and offers robustness in dealing with

distorted license plate.

References:

[l] . P,Comelli, P.Ferragina, M.N.Granieri, and F.Stabile, "Optical recognition of motor vehicle

license plates", IEEE Transactions on vehicular

technology, vo1.44(4), p790-799, Nov 1995

* 1411



[2]. Y.Cui, Q.H uang, “Extracting cha racters of license

plates from video sequences”, Machine Vision

and Applications, vol. lO(5-6) 1998, p308-320

[3]. Kamat, Varsha and Ganesan, “Subramaniam

Efficient implementation of the Hough Transform

for detecting vehicle license plates using DSP’s”,

Proceedings of Real-Time Technology andApplications, May 15-17 1995, p58-59

[4]. T.Sirthinaphong and K.Chamnongthai,

“Extracting of car license plate using motor

vehicle regulation and character patterrecognition”, Proceedings of IEEE Asia-Pacific

Conference on Circuits and Systems, 1998, p559-

56 2

[SI. E.R.Lee, P.K.Kim, and H.J. Kim, “Automatic

recognition of a car license plate using color

image processing”, Proceedings of IEEE

International Conference on Image Processing,

NO V13-16 1994, ~3 01 -3 05

[6]. S.H.Pa rk, K.I.Kim , and K.Jung , and H.J.Kim,

“Locating car license plates using neuralnetworks” , E lectronics Letters vo1.35( 17), 199 9,

p 1475-1477

[7]. Zunino, Rodolfo, Rovetta and Stefano, “Visual

location of license plates by vector quantization”,

Proceedings - IEEE International Symposium on

Circuits and Systems v 4 May 30-Jun 2 1999

[SI. S.K.Kim, D.W.JSim, and H.J.Kim, “Recognitionof vehicle license plate using a genetic algorithm

based segmentation”, Proceedings of IEEE

International Conference on Image Processing,

[9]. T. Naito, T. Tsukada, K.Yamamd, K. Kozuka and

S. Yamamoto, “Proceeding of InternationalConference of Interlligence, Information and

Systems, Aug 1999, USA.

[lo]. S. Messelodi, C.M. Modena, “Automaticidentification and skew estimation of text lines

inreal scene im ages”, P attern Recog nition, Vo1.32

[ l l] . Kenneth R. Castleman, “Digital imageprocessing” , Prentice-H all International, Inc.

1999 IEEE p IV-135-IV-138

Sep 16-19 ‘1996,p 661-664

1999, ~791 -81 0

Ori ina

PreprocessingHistogram Equalization

hConstrast Enhancement

Extraction of

Candi at ed Regions

Filter

Value algorithm

LFinal

Result

Fig. . The major steps of the algorithm.



Fig.2. The illustration of the algorithm: (a) The input image with poo r illumination ; (b) The effect of

preprocessing operation; (c) The result of Sec.2.3; (d) The extracted licen se pla te region in Sec 2.5, 2.6,

which is bound by a skew rectangle: (e) The resultof

the algorithm. The area bound by a whiterectangle is the candida te car license pla te detected by the algorithm.



plate detected'by the algorithm.

\

1414 -

car license plates detection from complex scene

Documents