TABLEII

Get Nf If NF> MaxF

For each character segme nts Calculate the medium point M i

For each two con secutive medium points Calculate the distanee DK Calculate the mi nimum dista nee

Merge the character segme nt k and the character segme nt k +1 NF = NF - 1 End of algorithm

where Nf is the nu mber of character segme nts, MaxF is the nu mber of the lice nse plate, and i is the in dex of each character segme nt.

The medium point of each segme nted character is determ ined by:

+ ^2

(3)

and Si2 is the final coord in ate

where is the in itial coord in ates for the character segme nt,

for the character segme nt. The dista nee betwee n two con secutive medium points is calculated by:

(4)

(u)

Fig.6 The

segme ntati on results

B. Using specific prior knowledge for recognition

The layout of the Chin ese VLP is an importa nt feature (as described in the sect ion II),

which can be used to con struct a classifier for recog nizing. The recog nizing procedure adopted conjugate gradie nt desce nt fast lear ning method, which is an improved lear ning method of BP neural network[10]. Conjugate gradient descent, which employs a series of line searches in weight or parameter space. One picks the first

desce nt directi on and moves along that direct ion

un til the minimum in error is reached. The sec ond desce nt direct ion is the n computed: this direct ion the “ conjugate direct ion ” is the one along which the gradie nt does not cha nge its direct ion will not “ spoil ” the con tributi on from the previous desce nt iterati ons. This algorithm adopted topology 625-35-N as show n in Fig. 7. The size of in put value is 625 (25*25 ) and in itial weights are with ran dom values, desired output values have the same feature with the in put values.

Input X XI

X2 … Xi …K625

Fig. 7 The n etwork topology

As Fig. 7 shows, there is a three-layer n etwork which

contains worki ng sig nal feed forward

operati on and reverse propagati on of error processes. The target parameter is t and the len gth of n etwork output vectors is n. Sigmoid is the itialized

with ran dom values, and cha nged in a directi on that will reduce the errors.

The algorithm was trained with 1000 images of differe nt backgro und and illu min ati on most of which were degrade severely. After preprocess ing process, the in dividual

characters are

non li near tran sfer fun cti on, weights are in

stored. All characters used for training and test ing have the same size (25*25 ).The in tegrated process for lice nse plate recog niti on con sists of the follow ing steps:

1) Feature extract ing

The feature vectors from separated character images have direct effects on the recog niti on rate. Many methods can be used to extract feature of the image samples, e.g. statistics of data at vertical directi on, edge and shape, framework and all pixels values. Based on exte nsive experime nts, all pixels

values method is used to con struct feature vectors. Each character was

reshaped into a column of 625 rows ' feature vector. These feature vectors are divided into two categories which can be used for training process and testing process.

2) Training model

The layout of the Chin ese VLP is an importa nt feature, which can be used to con struct a classifier for training, so five categories are divided. The training process of nu mbers is show n in Fig. 8.

i n pul

fknluix vector

Fig. 8 The architecture of a n eural n etwork for character recog niti on As Fig. 8 shows, firstly the classifier decides the class of the in put feature vector, and the n the feature vector enters the

neural network correspondingly.

After the training process the training and testi ng process is

optimum parameters of the net are stored for recog niti on. The summarized in Fig. 9.

(a) Training process

(b)Test ing process Fig.9 The recog niti on process

3) Recog nizing model

After training process there are five n ets which were completely trained and the optimum parameters were stored. The untrained feature vectors are used to test the n et, the performa nee of the recognition system is shown in Table III. The license plate recognition system is characterized by the recog niti on rate which is defi ned by equati on (5).

Recognition rate =(number of correctly read characters) / (number of found characters) (5)

TABLE III

Class Number Letter Chin ese character Number and letter

Recog niti on 99.5% 97.4% 96% 97.3% 98.2% Special character IV. COMPARISON OF THE RECOGNITION RATE WITH OTHER METHODS

In order to evaluate the proposed algorithm, two groups of experime nts were con ducted. One group is to compare the proposed method with the BP based recog niti on method [11]. The result is show n in table IV. The other group is to compare the proposed method with the method based on SVM [12].The result is show n in table V. The same training and test data set are used. The comparis on results show that the proposed method performs better tha n the BP n eural n etwork and SVM coun terpart.

TABLE IV Method Our method

Chin ese character 96% 94. 5% TABLE V Number 99.5% 97.6% Letter 97.4% 89.8% BP Method Our method SVM Chin ese character 96% Number 99.5% Letter 97.4% 95.7% 93. 7% 99.5% V. CONCLUSION In this paper, we adopt a new improved learning method of BP algorithm based on specific

features of Chinese VLPs. Color collocation and dimension are used in the preprocessing procedure, which makes location and segmentation more accurate. The Layout of the Chinese VLP is an important feature, which is used to construct a classifier for recognizing and makes the system performs well on scratch and inclined plate images. Experimental results show that the proposed method reduces the error rate and consumes less time. However, it still has a few errors when dealing with specially bad quality plates and characters similar to others. This often takes place among these characters (especially letter and number):3 — 8 4—A 8—B D —0. In order to improve the incorrect recognizing problem we try to add template-based model [13] at the end of the neural network.

REFERENCES

[1] P. Davies, N. Emmottand N. Ayland ,“ License Plate Recognition Technology for Toll Violation Enforcement ” Proceedings of IEE Colloquium on Image analysis for Transport Applications , Vol . 035, pp. 7/ 1- 7/5, February 16, 1990.

[2] V. Koval , V. Turchenko, V . Kochan, A. Sachenko, G. Markowsky , “Smart. License Plate Recognition System Based on Image Processing Using Neural Network ” IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing System: Technology Applications 8- 10 September 2003.

[3] Abdullah , S.N.H. S.; Khalid , M. ； Yusof , R.; Omar, K. “ License Plate Recognition using multi - cluster and Multilayer Neural Networks ” Information and Communication Technologies, 2006. ICTTA ' 06. 2nd Volume 1, 24-28 April 2006 Page( s): 1818 - 1823.

[4] Nathan, V.S. L.; Ramkumar, J.; Kamakshi Priya, S. “ New approaches for license plate recognition system” Intelligent Sensing and Information Processing, 2004. Proceedings of International Conference on 2004 Page(s): 149 - 152.

[5] Mei Yu; Yong Deak Kim , “ An approach to Korean license plate recognition based on vertical edge matching” Volume 4, 8-11 Oct. 2000 Page(s): 2975 - 2980 vol.4.

[6] Tindall, D.W. ” Application of neural network techniques to automatic licence plate

recognition ” Security and Detection, 1995., European Convention on 16-18 May 1995 Page(s): 81 - 85.

[7] Aghdasi, F.; Ndungo, H. “Automatic licence plate recognition system” AFRICON , 2004. 7th AFRICON Conference in AfricaVolume 1, 2004 Page(s): 45 - 50 Vol. 1

[8] Richard O. Duda Peter E.Hart David G.Stork, “Pattern Classification Second Edition ”

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