Design of License Plate Recognition System

Design of License Plate Recognition System Based on the Adaptive Algorithm
Liu Ying Li Nannan Department of applied mathematics Department of pattern recognition and image processing Liaoning Provincial College Of Communications Shenyang Institute of Aeronautical Engineering Shenyang, Liaoning Province 110122,china Shenyang, Liaoning Province 110136,china lyg81996@ linannan_100@
Abstract - A new recognition system for the car license plate is designed in view of the low accuracy and the lost time of the identification program in a com plex environm ent, such as low and varying light or strange noisy. The algorithm is composed of five steps: the im age preprocessing, the position and segm entation of the license plate, the characters cutting, the feature extraction and the character recognition. It extracts the features based on the m ulti-scale wavelet. The recognition for characters used the RBF neural network. The new adaptive strategy for raising the speed of recognition is proposed by decreasing the num ber of som e existing hidden layers and the dim ension of som e features from the wavelet. The experim ent results of the actual im ages show that the average recognition rate can reach more than 92% and the average recognition spend is less 0.11 s than the existing RBF algorithm.
Key words - license plate recognition, characters cutting, wavelet transform, neural network
Intelligent traffic monitoring and management systems have become the main direction of traffic management, and the automatic identification of vehicle license is an important research topic and the key technologies of intelligent transportation. EunRyung [1]use color component of images for vehicle license target identification, which is simple, but low recognition rate, the average recognition rate is about 85 percent; the average recognition rate of Luis [2]’s system can reach 90 percent, but due to complex algorithms, high recognition precision is with recognition speed as a sacrificial cost. This paper proposes a new license recognition system to identify the shortcomings of low-precision and slow recognition speed.
The entire license recognition system comprises five parts: image preprocessing, the license positioning and segmentation, character cutting, feature extraction and character recognition. Following is presentation of every part.
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License plates have many different colors and the brightness have a greater impact on them, so the images must be normalized before feature extraction. Image pretreatment technology is to enhance regional characteristics of plates and reduce random interference. Firstly, we use weighted average method to gray process the images, so that the effect of color can be avoided; and then we enhance the images by gray balance to reduce the interference of brightness; finally in order to save storage space and speed up the computing, we adopt binarization processing through an adaptive threshold method. If the plate exist tilt situation, Hough transform can be used to extract tilt angle and do correction by rotating
images. Pretreatment results are shown in Figure 1.
(a) Original image (b) Gray images (c) Gray balance
image
(d) Binarization image (e) Corrected image
Fig1 Preprocessing images
After pretreatment, we can continue to locate and segment the license plate image.
Ċ 3/$7(6 326,7,21,1* $1' 6(*0(17$7,21 License plate image is composed of text and figures, so it has a clear distribution law. Here we presents a plate positioning method based on horizontal scanning and vertical projecting, which make full use of the features that the hopping between characters region and background region has obvious differences
First of all, horizontal scanning the whole image, taking the number of image pixels hopping records as a basic feature. The plate has seven characters, each character will appear at least four times hopping, and seven characters are twenty-eight times. This can be used for set a transition threshold T.
After the horizontal scanning, the images would still exist on some regions which have similar levels to the horizontal regions. These pseudo-plate regions need to eliminate through the vertical projecting. As shown in Figure 2 (a) (b), the vertical projecting image of plate region have a clear rule for the peak - Valley - peak, and amplitude is far higher than other regions, but this rule is not clear in pseudo-plate region. The region boundary can be sure according to the vertical projecting and the prior knowledge of the ratio between length
978-1-4244-2503-7/08/$20.00 © 2008 IEEE
and width. The result is shown in Figure 2 (c).
(a) Plate region (b) Pseudo-plate region (c) Region boundary image
Fig 2 the image of horizontal scanning and vertical projecting
Through the plate positioning, we can have precise plates,
so the next step is to cut characters [3].
ċ CHARACTERS CUTTING
Because the characters of license plate have the same size
and certain symmetry, so we can use the strategy combined
crude cutting and fine cutting to cut characters. In crude
cutting, we find the minimum points through image histogram
analysis. These minimum points have a certain balance.
Because the number of characters in a plate ranges 6-8, so the
minimum number of points should be among 5-7. If the
number is inadequate or excessive, the necessary adjustments
should be made to meet the requirements, and located this
column of minimum points as segmentation department, then
cut plate into small character images in accordance with the
cutting position. Fine cutting can remove extra edge around
characters. For each characters image, from around to scan
both horizontal and vertical directions, and get rid of the row
or column which is less than the threshold value. Cutting
results are shown in Figure 3.
(a) Crude cutting (b) Fine cutting
Fig 3 Characters cutting images
ČFEATURE EXTRACTION BASED
ON THE MULTI-SCALE WAVELET
Due to the font norms of characters in the license plate,
we can use multi-scale wavelet transform to analyze features
from different characters. According to the standard characters
in image, using multi-scale wavelet analyzing to extract all
possible features of characters in the license plate, every
feature of the
characters compose the feature vector, so they can be put in
neural networks for recognition.
˄1˅Multi-scale wavelet transform [4]: Selecting the
features of the characters, the key is the details of strokes of
each characteristics, it can show the selective of direction in
multi-scale wavelet decomposition, so we selected the feature
of mean, maximum, standard deviationists and so on in
low-frequency components, the detail of horizontal, the detail
of vertical and the detail of diagonal. We used a tower fast
wavelet algorithms in multi-scale wavelet transform. Wavelet
transform of characters and feature extraction process is
shown figure 4:
(a)Original image (b)Decom Image (c)Low-frequency image
(d)Detail horizontal image (e) Vertical detail Image (f) Diagonal detail Image
Fig 4 Image of wavelet decomposition of number 0
Reconstructed detail at horizontal clears the details of
level and gets some pixels on horizontal direction;
reconstructed detail at vertical is the components map from
high-frequency and low-frequency, so the details of vertical
direction can be expressed obviously. Reconstructed detail at
diagonal is all components of high-frequency, so it is sensitive
to the information of diagonal.
First of all, we use wavelet transform on some standard
characters; we can get some different features from different
characters in table 4.3. For example, selecting the features of
number 0 from the standard template characters.It is shown in
table 4.
Table 4.1 The eigenvalue of wavelet coefficients in number 0 from the standard template of characters
Template 0
The weight of approximate The details of horizontal The details of verticals The details of diagonal mean maximum
standard
deviation
mean maximum
standard
deviation
mean maximum
standard
deviation
mean maximum
standard
deviation Eigenvalue 1.483 2.125 0.4843
0.000
98
0.6289 0.07742
0.000
468
0.5977 0.1368
0.00
0184
9
0.3125 0.04136
˄2˅Extracting the eigenvector: We treat the
characters which are extracted from the license plate by
wavelet decomposition. We select the number 0 from
three Chinese license plates, such as the ‘hei AT0697’,’su
E04220’ and ‘su A47809’. We can see the eigenvalue in table
4.2.
Table 4.2 The eigenvalue of wavelet coefficients from the testing characters
Feature The license plate
The weight of approximate
The details of horizontal
The details of verticals
The details of diagonal
M MM SD M MM SD M MM SD M MM SD Number 0 in the license plate 1 1.217 2.261 0.3917
0.001680.5261 0.05084
0.009
02 0.5962 0.1137
0.00
0158 0.2816
0.02774 Number 0 in the License plate 2 1.453 2.092 0.4206
0.0041
60.4825 0.1257
0.008
156 0.6381 0.2085
0.00
0308 0.5482
0.1089Number 0 in the license plate 3 1.321 2.1069 0.4267
0.0009
12 0.4837 0.08883
0.003
82
0.7431 0.1576
0.00
0375
0.2695
0.0572
Mean
1.33033
2.1533 0.413
0.0022
5
0.4974 0.088457
0.0069
0.6591
0.15993
0.00002
0.3663 0.064
613
As table 4.1 and table 4.2 shows, we match template after extracting features from the templates and license plates, if the degree of relevant can meet the requirements, it can be said that the characters of license plate and characters of templates are similar to each other. According to the data from table 4.1 and table 4.2, calculating the correlation coefficient(r) between them, we can get the result that r = 0.986 by the Matlab. So the number 0 from license plate is very similar to the template, it can be recognition easier.
The size and resolution of the characters in the license plate are different,so the template matching is also varied: If the cutting area of the characters close to the size of the
template, and the value of characters abstained from the wavelet analysis is similar to the template’s, the correlation coefficient can reach 0.982;but if the size of cutting characters is not close to the template's,it should be enlarged or narrowed in some extent, after that continue to analyze these characters, we get correlation coefficient r=0.978.The above shows that the size of the template has little effect to the characters of the license plate, analyzing character by the mean of wavelet has a high similarity to the template, so it is familiar to the recognition in neural network.
We can see the eigenvalue of template characters in table 4.3 (Due to space constraints, only part of some numerical):
Table 4.3 The eigenvalue of wavelet coefficients from the standard template of characters
Feature The characters
The weight of approximate
The details of horizontal The details of verticals The details of diagonal M MM SD M MM SD M MM SD
M
MM SD
0 1.483 2.125 0.4843 0.000980.62890.07742 0.000
468 0.5977 0.1368 0.0001849 0.3125 0.04136
1 1.558 2.190
2 0.3436 0.00042
3 0.50360.07195 0.008
50.5968 0.1248 0.000342 0.2534 0.05904
2 1.6 2.3256 0.2947 0.00019
3 0.50050.1506 0.0001623
0.5861 0.1503 0.00025 0.2913
50.1109
3 1.677 2.2593 0.2528 0.001060.49352 0.1648 0.000
4169 0.4931 0.1154 0.000354 0.2638 0.107
4 1.392 2.1359 0.3434 0.0006352 0.50621 0.1368 0.000
2571
0.5531 0.1531 0.000375 0.1924 0.05927
… … … … … … … … … … … … …
A 1.359 1.926 02834 0.000160.315 0.1261 0.00180.4169 0.2128 0.000185 0.1734
80.08186
B 1.356 1.8349 0.3259 0.000289 0.68230.1483 0.004
527 0.5617 0.1752 0.0002249 0.1617 0.0728
C 1.192 1.685 0.2596 0.014920.48290.127 0.004
275 0.5429 0.2937 0.0005137 0.3518 0.06485
… … … … … … … … … … …
… …
hei 1.483 1.5942 0.3379
0.0002
91 0.58190.1572 0.00810.9841 0.2841 0.000548 0.3817 0.1271 su 1.35 2.092 0.3613
0.0002
196
0.3618
0.09573
0.001681
0.7834 0.1692 0.000348 0.2719 0.06278
… … … … … … … … … …
…
… …
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The recognition of characters on vehicle license plate is a nonlinear separation problem .The input samples may have high dimension ,and they will fold in the feature space ,using simple linear classifier can not separate them .So In this article we use neural network[5] to separate them.
The topological structure of RBF neural network can be designed according to actual problems, its learning rate is fast, and dose not exist the problem of local minimum, so it has much superiority than BP neural network, and fit for pattern recognition and classification. RBF neural network is a special Feed-Forward Neural Network, it has three layers: input layer, hidden layer, output layer. The transformation between the input space and hidden space is nonlinear, but the transformation between the hidden space and output space is linear. The activation functions of hidden layers are Gaussian functions. When the input is in the appointed range, the hidden layers can response actively. In this article the number of input layers is 10 and the number of output layers is 1, we choose different eigenvalues of 12 dimension victor by wavelet analysis. Through many tests, we delete ‘max’ eigenvalue and ‘mean’ eigenvalue. At last we reserve 7 eigenvalues, the results show that this method is accurate enough.
We collect 550 samples (num: 0, 1,…, 9) and 10 classes, each class contains 55 characters (400 is used to train, 150 is used to test).We use molding board centers of normal 0-9 as the initial centers and use competition learning algorithm to train this network. When a sample is not similar to the nearest center, add a hidden layer and alter this center, until the number of iterate arrives the appoint number or the network arrives convergence. The initial number of hidden layers is 10, the max number of hidden layers is set to 35, and the time of training is set to 8000.
After 5000 times of iterate, we get the 29 clustering centers. Distribute every vector to its nearest clustering center, then use gradient descend method to train weights. When using MATLAB to simulate, we earn a satisfying result finally: the average discrimination of 0-9 reach 93.6%.
To a recognition way, we must consider the utilization rate of the resource and the need of real-time characteristic excepting the recognition rate. This paper improves the recognition speed by decreasing the hidden cells and that means optimizing the structure of network through deleting unimportant hidden cells.
This paper realizes the adjustment of network structure by using the final prediction error criterion RE of Akaike. After using deleting strategies by adopting FPE, the number of hidden cells is 20 and the recognition rate is decreased a little (the average recognition rate is 92.8%), but it still can be received for the tidy network.
From the above experiment, we know that the recognition rate decrees very small, but the recognition speed can be improved a lot. When we don’t delete cells and the dimension of character vectors, the average recognition time is 0.38s, but the average recognition time is 0.27s when using FPE to delete. So the recognition time is cut down 0.11s. The average recognition time in the whole process of vehicle license plate recognition is 0.46s in this algorithm, so it can satisfy the character of real-time.
The average recognition rate of each character is shown
in figure 5, and the time of comparing between before deleting cells and the decreasing dimension and after is shown in figure 6.
[5] Guo zhaoqiu, Zhao yuelong ,Application of wavelet transform and neural
network in vehicle license plate recognition , Information technolghy, vol .11,2005.。