Abstract— This paper introduces a priority based edge

detection of license plate and recognition of characters. We intend to extract and identify vehicle registration numbers from real time vehicle images without any human intrusion. In computation of license plate, localization shows better edge detection result when compared to conventional methods such as binarization, template matching or transition mapping. Although several effective image processing algorithms are created nowadays, real time operation is found to be impractical as their application is limited by huge computation requirements. As processing time and illumination of the image affects real time operation, it is reduced by setting a dynamic threshold. This paper uses the concept of localization for license plate extraction from real time images followed by the segmentation of characters and reformation. Keywords: priority based edge detection, localization, license plate, dynamic threshold, segmentation.

I. INTRODUCTION ith everything around us being automated by computer, computer proves itself to be more effective in the field of security by its implementation in biometrics, vehicle

monitoring etc. As vehicle monitoring involves identification of the vehicle, Automatic license Plate Recognition (ALPR) can be used in vehicle tracking, surveillance, red light violation, tollbooth etc. Unlike license plate recognition in tollbooth which involves detecting single plate, real time implementation of ALPR for red light violation involves an algorithm that detects multiple license plates. Identification of license plate involves three steps: license plate detection, character segmentation, and character recognition. Of all the steps, license plate localization that comes under the license plate detection step is the most important as its accuracy influences the character recognition and it is more time consuming as it has to go through the image as a whole. Searching the license plate using neural network [1] for localization consumes more time. As a result a linear algorithm with effective identification algorithm is required. Usually for locating the license plate, morphological operations [2] like open and close are used which requires definition of a structural element to open or close only a certain structure. As it is difficult to define a different structural element dynamically, using morphological operators like open or close

may result in less accuracy. Some of the other conventional methods used are binarization and edge detection. For extracting the plate region, edge detection with smearing algorithm is used. Edge detection algorithm counts the number of edges in the same row of a real time image of license plate (unwanted edges are also taken into account). It is also seen that smearing algorithm is used for extracting the plate region. Smearing algorithm counts the number of whites in image and when the count of white is found to be between a previously set upper and lower bound, that row is considered horizontally and vice versa vertically. Smearing algorithm is almost static and can’t be used if license plate varies from its expected size. Hence this technique is impossible for real time use. It can be used in application when the size is known already. Its attainment depends on the size being predefined manually. Smearing is not effective for multiple license plate recognition when multiple plates are straight to each other. Distance of camera from the vehicle also thwarts the application of this method. Lastly it is noticed that a statistical based template matching is used for character recognition. Statistical based template matching depends on the accuracy of the template; hence it’s not applicable for real time which requires dynamic algorithm. Using more templates may increase accuracy but with sacrifice of time and hence it is ineffective. In order to reduce the regions detected as license plate, we propose a method that detects horizontal and vertical edges of the license plate with different priority. This approach has been tested in a real time application and integrated satisfactorily into a complete system for license plate identification. This upgrading also proves to be very suitable for both localization accuracy and coding effectiveness.

II. LICENSE PLATE DETECTION Our proposed method consists of six main steps: Preprocessing, identification of license plate, license plate localization, dynamic thresholding, character segmentation and character recognition.

A. Preprocessing In this method, the traditional algorithm [3] is improved by using histogram equalization to enlarge the intensity difference among objects and background. In our experiment, license plate images are tested under different light conditions. Histogram equalization is used to make the night images clear. When yellow plates are used for detection, there is difficulty in

Multiple License Plate Recognition for Intelligent Traffic Management

Zubin Abraham.V, Betsy Kurian, and Rajesh. N CITE, Manonmaniam Sundaranar University, Tirunelveli, India.

Email : vzubin@gmail.com, cinbets@gmail.com, rajeshmtechict@gmail.com

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detection of yellow plates as they contain a low value in blue component. We resolved this difficulty by integrating histogram equilization. Two reasons for using histogram equilization are:

1. At night, illumination is low. Some preprocessing is required. 2. For yellow plates the difference between foreground (characters) and background (yellow region) are very low as the yellow region of the image will be much closer to black which is the foreground color.

The difference in intensity (yellow region) between foreground and background is identified using this method. But this creates speckles in the image. As speckles produced

more number of edges, the time taken for processing the license plate region increased. A smoothening algorithm is used to level the speckled region.

B. Identification of license plate The proposed algorithm works on the horizontal and vertical

edge information of the image, which typically occurs in outdoor environment under varied illumination condition. As in a typical road image, the horizontal and vertical edges are not much closely located, except in the license plate region which has the characters more close together, due to the presence of text that makes both horizontal and vertical edges

to be more closely located. Non license plate areas contain less closely placed horizontal and vertical edges. And thus we get a reduced region for tracing the information from license plate.

As seen in figure 2, the license plate region contains more

closed edges. It is difficult to process a license plate point by point. It requires an analysis of a group of points. This algorithm works by finding a cluster of edges placed together. In order to achieve this, the image is divided into blocks. In each block number of vertical edge is counted. If the vertical edge exceeds a certain threshold, horizontal edge is counted. Now if horizontal edge exceeds a certain threshold, the block is considered as license plate region. In order to detect multiple license plates, it is necessary to search the whole image for possible license plate region.The process is iterated over all the sub blocks. If wb is the width and hb is the height of each block, the edge count is taken for each block for both vertical and horizontal edge images. For each region Ri

1 and n , 0 . (1)

A region is considered to contain license plate if its value is

1 else it is discarded. and are the number of edge values in horizontal edge image and vertical edge image respectively. The vertical edge is given more priority than horizontal as the vertical edges are less dense and is good for detecting license plate but still it is not true for all cases, as in some case vertical edges come near to the license plate, e.g. truck and van. Therefore to make the algorithm more accurate and to reduce the regions detected, horizontal edges are also considered. The horizontal edge is counted only if crosses a certain threshold t.

C. License plate localization Once the valid blocks have been found, it is necessary to

clip the selected blocks with the license plate region in it. The blocks that are close together are grouped to form a license plate region. The vertex coordinates of each region are found and so is the height and width of each region. The region that is too big is discarded as there is a possibility of exceeding the size of standard license plate and so this cannot be a valid license plate region. Even if some of the non license plate regions are selected, they can be omitted. But still, if few

(a) (b)

Fig. 2. (a)Binarized vertical edge detected image. (b)Binarized horizontal edge detected image.

(a)

(b) Fig. 1. (a) Grayscaled night image (b) Histogram equalized image

regions slip past and get selected, they will be found invalid during the segmentation and recognition phase. This algorithm has been successful in reducing the unwanted regions being selected and also successful in locating multiple license plates.

D. Dynamic Thresholding For further processing of the Indian license plate, it is

necessary for the extracted plate to be binarized. As it is easy to process in dual color mode and there exists only two components in the license plate; where one of the component is background of the license plate which is yellow or white in color and the second one is foreground of the license plate containing black license plate characters common to both yellow or white background of the license plate. In order to binarize the license plate, a dynamic threshold is to be set that has to be varied from image to image depending upon the illumination. We propose an algorithm to dynamically

calculate the threshold value. The algorithm is as follows:

For each pixel (i,j) in f(x,y)

Δmin=abs(f(i,j)-min) Δmax=abs(f(i,j)-max) If Δmin< Δmax then min=tmin*steps (2) If Δmax< Δmin then max=tmax*steps

End Threshold= (min+max)/2

where, steps = -log (1+xt) and x is a constant. t increments on every iteration and thus equation takes faster step towards the max and min in the beginning and smaller steps later so that the value of min and max converges with the average of background and foreground pixel value respectively, initially the max and min values were set to 0 and 255 respectively. The proposed algorithm is designed to set the values, min and max to the foreground and background respectively. As the license plate image is iterated pixel by pixel, the values of min and max moves towards the foreground and background. Finally the average of max and min values will end up in an ideal threshold value for better binarized license plate image.

E. Segmentation Character segmentation is the fundamental step performed

before character recognition. After threshold calculation and binarization of the image, the image is clipped off from the unwanted boundaries formed by black pixel around the license plate which is done by converting the pixel values to white. Each character regions are determined by identifying the horizontal projection of number of black pixels from the license plate followed by clipping of the first row characters. This is further reduced into separate characters by finding the vertical projection of number of black pixels for character separation, so as to obtain the separated license plate characters in the first row. In most of the vehicles like van, truck, bike etc., license plate includes two rows. For such cases, horizontal projection is carried out again in search of further existence of any character in the license plate. On re-test, the characters are again segmented using vertical projection. Finally, the

Fig. 6. Histogram graph of a license plate showing the difference in background and foreground color

Fig. 4. Localized regions of input image (a)

Fig. 5. Localized regions of input image (b)

(a) (b)

Fig. 3. (a),(b) Input images

separated characters are processed for identification of the character using a character recognition algorithm.

F. Character recognition Each segmented characters are compared with a predefined

database. The predefined database consists of all characters and number that will possibly appear in the standard license plate. The segmented characters are checked with the database using the following algorithm: 1. Resize the test image and the database image to equal image size. 2. Check for each pixel

If f(i,j) == 0 or t(i,j)== 0 tn=tn+1 if f(i,j)==t(i,j) (3) n=n+1 end

end correlation(x)=n/tn

Here x denotes the database image and n is the number of black pixel. tn is the total number of pixel. Step 2 is iterated over each image in the database and the closest correlation is found. The recognition is made more accurate by considering only the black pixel present in the image.

III. EXPERIMENTAL RESULTS We tested the proposed system on different images captured in various intensity of light/day illumination. The performance of the proposed method was analysed on different images. We found it good in detecting multiple license plates irrespective of the position of the license plate, with a success rate of 95%. The algorithm has managed to give high-quality result and also have successfully suppressed the false regions. The time-complexity of the detection has been greatly reduced in ranking and localization phase.

IV. CONCLUSIONS AND FUTURE WORKS We have presented a method that localizes and recognizes multiple license plates using vertical and horizontal edge detection. The algorithm developed gives an accuracy of 97% detection rate. The experimental results are satisfactory. Nevertheless, for future extension factors such as motion blur, and license plate frame can be addressed.

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Fig. 7. Segmented image of a valid localized image