• Journal of Korea Society of Industrial Information Systems
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• v.13 no.4
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• pp.1-13
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• 2008

In this paper, we propose a method to solve a problem in binarizing the rear number plate image captured by a camera on a moving vehicle. An image may be shadowed by the cavernous structure of the rear side of a moving vehicle and it makes us hard to get a high quality of binary image. Therefore, we first detect a shadow edge and then divide an image into the shadow part and non-shadow part by the edge. Finally, the binary image is obtained by binarizing each part and merging them In this paper, we do comparative work on a group of binarization methods including our method, the method suggested by Zheng, the method using block binarization, and the method using labeling. The result shows that our method achieves better performance than others in most cases.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.56-63
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• 2014

This paper deals with a binarization for plate number recognition. The binarization process converts an image into a binary image and plays an important role for automatically recognizing plate number. The rear license plate has often a shadowed image which causes erroneous binarized image due to non-uniform illumination. In this paper, a binarization method is proposed in which the shadow line is detected in a rear plate with a shadow. And then the histogram matching is conducted for the two image separated by the shadow line. After histogram matching, two images are joined and finally Otsu method is applied for the binarization. In the experiment, the proposed algorithm shows robust performance compared to the conventional method in the presence of estimation error in the shadow line.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.400-404
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• 2006

We suggest a method to detect rear car license plate of one more cars by using blobs. First, we try to search all of the blobs from an input image based on the difference between objects and background. Second, we obtain rectangles enclosed the blobs, and rectangle clusters by considering the properties, for example, the number, size, distance, position. Third, the cluster is verified by the Support Vector Machine. Even if we only use the adaptive binarization as the preprocessing, the detection ratio is very high.