Recognition of Gray Character Using Gabor Filters Peifeng Hu, Yannan Zhao, Zehong Yang, Jiaqin Wang
State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology TsingHua University, Beijing, P.R.China Email:
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[email protected](author4) Abstract - In this paper, a novel Gabor filter-based feature extraction method for low resolution gray character classification is proposed. By applying Gabor filters to a character image, dominant orientation matrix is obtained and used to form feature vector for recognition. We compared our Gabor feature with other Gabor features. Experiments show that the proposed feature extraction method achieves high recognition accuracy and is also not sensitive to noise and other distortions. This method has been used in a Vehicle License Plate Character System.
characters.
Keywords: character recognition, Gabor filter, feature extraction, dominant orientation matrix.
1
Introduction
After several decades of research, many advances have been achieved in the area of character recognition field [1,2]. There are some effective character recognition approaches including artificial neural network[3], learning vector quantization[4] and support vector networks[5]. As we have known, the performance of a character recognition system is based significantly on the recognition feature used. Now recognition features fall into two main categories: Structural features and frequency features. Structural features include direction feature[6], direction change feature[7] and skeleton feature[8]. Structural features can precisely describe the structure of a character and success to be used in the recognition of handwritten characters. But they are vulnerable to the recognition of low resolution gray characters such as characters in video index or characters in vehicle license plate. It is difficult to extract invariability structural features because of deform and variation existing in low resolution gray characters. Another feature extraction method--frequency feature extraction method such as Fourier transform[9] and wavelet transform[10] are widely used for the recognition of low resolution gray character and are proved to be very effective. Figure 1 gives an example of this kind of
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Figure 1. Low resolution gray character example Gabor Filter, a kind of frequency filter, which has been applied to texture analysis[11], moving object tracking[12] and face recognition[13], are also shown to be good fits in character recognition field. Daugman[14] discovered that simply cells in the visual cortex can be modeled by Gabor filter. The 2-D Gabor filters proposed by Daugman are local spatial filters that conjoin information in the 2-D spatial and 2-D Fourier domains. Gabor filter performs a spatial frequency analysis on image. It can extract oriental-dependent frequency contents as small as possible. Hamamoto and Uchimura[15] proposed a Gabor filter-based feature extraction method for handwritten numeral character recognition. Tavsanoglu and Saatci[16] proposed an approach to form orientation map as recognition feature using a CNN Gabor filter. Yoshimura and Etoh[17] used Gabor jets projection to form a feature vector for recognizing low resolution gray-scale character. These applications show that the Gabor filter-based feature
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The control parameters λ , σ , M , N determine the property of Gabor filter. We will discuss the optimal value of these parameters in Section 4.
extraction methods are very effective in classifying characters. In this paper, we will give a novel Gabor filter-based feature extraction method and compare this method with other existing Gabor feature extraction methods. Section 2 gives the description of common 2-D Gabor filter. Section 3 shows this Gabor feature extraction method based on dominant orientation matrix. Section 4 discusses the selection of control parameters of Gabor filter and gives experimental results. In section 5, some conclusions are given.
2
As described above, Gabor filter can localize direction spatial frequency at θ . When applied to an image, the output responds maximally at those particular edges whose orientation is θ . That means Gabor filter is oriental selective to image. We can use this specialty to detect the edges at all orientations of an image. We convolve this image with a set of Gabor filters. The orientation angles of this set of Gabor filters are:
Gabor Filter
{θ k | θ k =
A common 2-D Gabor Filter is described by the impulse response:
h( x, y ) = g ( x ' , y ' )e jλ ( x cosθ + y sin θ )
(1)
3
Where g(x,y) is Gaussian function given by :
g ( x, y ) =
2
e
2πσ xσ y
( x ' , y ' ) = ( x cosθ + y sin θ ,− x sin θ + y cosθ ) (2)
λ
is the wavelength of Gabor filter and
orientation angle of Gabor filter.
σ x ,σ y
θ
is the standard
n-dimension vector where n is the number of
−( x2 + y 2 )
e
2π σ
2σ 2
e jλ ( x cosθ + y sin θ )
(3)
h( x, y ) to an image u ( x, y ) . The response output I ( x, y ) can be We apply this kind of Gabor filter
defined through the convolution sum: x+
I ( x, y , θ ) =
M 2
y+
N 2
∑ ∑ x1 = x −
− ( x1 − x ) 2 − ( y1 − y ) 2
u ( x1 , y1 ).e
2σ 2
N M y1 = y − 2 2
θk .
They
defined this vector as orientation map and used it as character recognition feature. Though orientation map can describe the orientation distribution of an image’s edge at different angle, it also lost the position information of each pixel in this image. But the position information is important for character recognition. Gabor orientation map feature is not very suitable for gray character recognition.
get :
h ( x, y ) =
feature
Tavsanoglu and Saatci[16] proposed the definition of orientation map. They counted the number of pixels that response maximally at θ k and then formed a
is the
deviation of Gaussian along the x − direction and y − direction. The spread of the two-dimensional Gaussian is decided by σ x , σ y . If we set σ x =σ y= σ , we can
1
Gabor filter-based extraction method
(5)
Hamamoto and Uchimura[15] directly used the response output of Gabar filter to a image as recognition feature. They applied four Gabor filters at orientation 0, π / 4, π / 2,3π / 4 to a character. Then they select the response output at some sampling points to form a feature vector.
− (( x / σ x ) 2 + ( y / σ y ) 2 )
1
kπ , k = 0,....(n − 1)} n
（4）
We use Gabor dominant orientation matrix[16] as recognition feature. For a X*Y image u , the Gabor dominant orientation matrix m is a X*Y dimension matrix. The value of m( x, y ) in dominant orientation matrix is valued by comparing the response output I 0 , I 1 ......I n −1 of image u convolved by the set of
u ( x, y ) obtain the maximum response output at an orientation θ k , then m( x, y ) is assigned the value k . Thus each element in matrix m is valued Gabor filters. If
* e jλ (cosθ ( x − x1 ) +sin θ ( y1 − y )) Where M , N is the dimension of Gabor filter.
between 0 and n-1. We get a X*Y dimension vector. We
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use this feature vector as character recognition feature vector. This kind of feature combines the position information and orientation information. It denotes the spatial orientation distribution. This feature inherits the specialty of Gabor features. We use it as recognition feature. In experiments we find it is not sensitive to small distortion and noise in character. The following is the procedure that extracts dominant orientation matrix as recognition feature: 1.Binarization and Normalization. First binarize the low resolution gray character and find the circumscribing frame of the character. Then extend the circumscribing frame to a 32*32 normalized image. 2.Apply
18
Gabor
filters
θ = 0, π / 18,....17 * π / 18
whose orientation is to the normalized image
and then obtain 18 response output I 0 , I 1 ......I 17 .
Figure 3.a.
Response output at
θ = 2π / 9
3. Provide the gabor dominant orientation matrix m where m is defined as :
m ( x, y ) = k
(6)
where max{I ( x, y,θ k )} for all k
Thus we obtain a new Gabor filter-based feature from character image. Figure 42 gives the procedure of this method. Figure 3 displays the response output of Gabor filter to a character at two different angles.
0 Figure 3.b. Response of Gabor Filter for Binarized Image
θ =π /2
2π / 9
normalizaiton
4
Provide Dominant Orientation Matrix
Gabor Transform
Experiments
We applied our Gabor filter-based method to 1835 low resolution gray characters. We randomly select 287 characters (about 10 characters for every class) as training characters. Hamamoto[12] pointed out that 1-NN classifier is the best choice for the Gabor filter-based feature both in recognition ratio and computation cost. So we use it as classifier.
π /2 13π / 18
As we have discussed, the Gabor filter-based feature extraction method requires setting control parameters of Gaor filter. We selected the optimal value for these parameters by experiments.
Figure.2 Feature extraction procedure
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4.1
Selection of λ and σ
The performance of Gabor features depends strongly on parameters λ and σ . The parameter λ determines the frequency spread of Gabor filters along angle θ . Appropriate selection of λ can make the filter narrow enough that will yield better results. Because Gabor filter is a kind of circular symmetry filter, optimal Gabor filter must make the orientation map of a circle pattern distribute equably at all orientations. So we apply Gabor filters to a circle pattern. Figure 5 gives the orientation maps of a circle at different value of λ and σ . We reached the optimal values λ = 1 and σ = 2 .
Figure 5.d. Orientation map for
4.2 Figure.5.a The circle image
Figure.5.b Orientation map for
λ = 1 and σ = 2 .
The dimension of 2-D Gabor filter
The dimension of Gabor filter is also important. The edge orientation of a pixel is associated with neighbor pixels. The dimension of Gabor filter must great enough to reflect all the influence of neighbor pixels to the pixel’s edge orientation. But increasing Gabor filter’s dimension will also increase computation cost. We must reach optimization between recognition accuracy and cost. Table 1 summarizes the test results using different dimensions Gabor filter in terms of recognition accuracies. In order to decrease computation cost, we use 16*16 character image to substitute original 32*32 original character image before convolving. Each pixel’s value in new image is the mean of four neighbor pixels in original character image. Then we apply the Gabor filter to the new 16*16 image. From this table, we found that 11*11 Gabor filter and 16*16 character image is enough for the recognition of low resolution gray character
λ = 1 and σ = 1.
Table 1. Summary of results using different dimensions The dimension of Recognition accuracy 2-D Gabor filter 32*32 character 16*16 character 7*7 90.11% 92.25% 9*9 94.54% 94.26% 11*11 96.25% 96.13% 15*15 96.66% 96.28%
4.3
Comparison with other Gabor features
We compared the result using Gabor dominant orientation matrix feature with other Gabor features at the same condition. Table 2 shows the experiment results.
Figure5.c. Orientation map for
λ = 2 and σ = 1.
422
[7] Masayoshi Okamoto, Kazuhiko Yamamoto, On-line handwriting character recognition using direction-change features that consider imaginary strokes, Pattern Recognition vol.32, no.7, July 1999
Table 2. Summary of results using different Gabor features Feature used Recognition accuracy Dominant orientation matrix 96.13% Gabor filter response output 94.56% Gabor Orientation map 89.98%
[8] JianMing Hu, Hong Yan, Structural primitive extraction and coding for handwritten numerical recognition, Pattern Recognition, vol.31, no.5, May 1998
From table 2, we find that Gabor dominant orientation matrix feature is more effective than other Gabor filter-based feature in the recognition of low resolution gray character.
5
[9] Guangyi Chen, Tien D. Bui, Invariant Fourier-wavelet descriptor for pattern recognition, Pattern Recognition, vol.32 no.7, July 1999.
Conclusions
[10] Wen L. Hwang, Fu Chang, Character extraction from documents using wavelet maxima, Image and Vision Computing, vol.16, no.5, Apr. 27, 1998.
In this paper, we applied a novel Gabor filter-based feature extraction method in the recognition of low resolution gray character. We obtained Gabor Dominant orientation matrix form response outputs of a set of Gabor filter to a character. We used it as recognition feature. We test this feature on a lot of characters. The test result shows this Gabor feature is very effective in character recognition.
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