car license plates detection from complex scene
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8/4/2019 Car License Plates Detection From Complex Scene
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Proceedingsof ICSP2000
Car License Plates Detection from Complex Scene
Da-shan Gao Jie Zhou
Department of Automation,TsinghuaUniversity
Beijing 100084,P.R.ChinaGaodsh@263.net 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.
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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.
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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
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[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
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[4]. T.Sirthinaphong and K.Chamnongthai,
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[SI. E.R.Lee, P.K.Kim, and H.J. Kim, “Automatic
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[6]. S.H.Pa rk, K.I.Kim , and K.Jung , and H.J.Kim,
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[7]. Zunino, Rodolfo, Rovetta and Stefano, “Visual
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[SI. S.K.Kim, D.W.JSim, and H.J.Kim, “Recognitionof vehicle license plate using a genetic algorithm
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[9]. T. Naito, T. Tsukada, K.Yamamd, K. Kozuka and
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Ori ina
PreprocessingHistogram Equalization
hConstrast Enhancement
Extraction of
Candi at ed Regions
Filter
Value algorithm
LFinal
Result
Fig. . The major steps of the algorithm.
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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.
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plate detected'by the algorithm.
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