• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1718-1721
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• 1997

An image processing algorithm is developed in order to recognize the type of cars, the position of a number plate and the characters on the plate. to recognize the type of cars, comparison of two images is used. One has a car image, the other is just a background image without car. After that recognition, a vertical line filter is used to find the location of the plate. Finally the simularity mehod is used to recognize the numbers on plates.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.2
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• pp.309-314
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• 1987

Using computer picture processing, a method of extracting the region of a car number plate is described. A modified Hough transformation, in which parameter plane is restricted, is proposed. The demerits of Hough transformation, i.e., it requires much computation time and storage capacity, are reduced by this method. Further, taking the features of a car number plate into consideration, the region of a car number plate is extracted.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.261-268
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• 2007

In this paper, We propose a new technique extract efficiently a car number plate based on edge projection. In order to obtain the region of car number plate, we use a motive that the luminance differences between the number plate background and characters. And, we introduce a projection technique to obtain character parts based on edge image. In vertical direction. we propose a shape matching method. Specially the new number plate standard has more characters than the old one in horizontal direction and, it is efficiently used to extract the number plate. Therefore, the proposed technique is useful to the new number plate standard. In simulation results. We have illustrated that our algorithm can recognize different number plates with a success ration of 90%.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.785-788
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• 2000

The recognition system of a car plate is largely classified as the extraction and recognition of number plate. In this paper, we extract the number plate domain by using a thresholding method as a preprocess step. The computation of the density in a given mask provides a clue of a candidate domain whose density ratio corresponds to the properties of the number plate obtained in the best condition. The contour of the number plate for the recognition of the texts of number plate is extracted by operating Kohonen Algorithm in a localized region. The algorithm reduces noises around the contour. The recognition system with the density computation and Kohonen Algorithm shows a high performance in the real system in connection with a car number plate.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.423-426
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• 1995

An image processing algorithm is developed in order to recognize the type of cars, the position of a number plate and the characters on the plate. To recognize the type af cars, comparison of two images is used. One has a car image, the other is just a background image without car. After that recognition, a vertical line filter is used to find the location of the plate. Finally the similarity method is used to recognize the numbers on the plates.

• Journal of Power System Engineering
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• v.5 no.2
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• pp.93-99
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• 2001

This paper presents the automatic recognition system for car number plate. In our country, two types of number plate pattern is used. The one is old type of number plate, the other is new type of number plate. To recognize both new and old type number plates, the system must have flexibility. Therefore, in this paper, automatic recognition system is developed by use of the neural network for good adaptation, good generalization, and modulation. And because the number plate is made of three codes, the multi neural network consists of three networks. Neural network is teamed by GDR(Generalized Delta learning Rule) and it is verified the effectiveness of the method through experimental results.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.100-108
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• 2016

In this paper, we propose a method to achieve improved number plate detection for mobile devices by applying a multiple convolutional neural network (CNN) approach. First, we processed supervised CNN-verified car detection and then we applied the detected car regions to the next supervised CNN-verifier for number plate detection. In the final step, the detected number plate regions were verified through optical character recognition by another CNN-verifier. Since mobile devices are limited in computation power, we are proposing a fast method to recognize number plates. We expect for it to be used in the field of intelligent transportation systems.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.6
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• pp.76-86
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• 1999

In this paper, it is shown that the car number plate are segmented and extracted more efficiently by using wavelet transform. A car image is decomposed by wavelet transform, and the high frequency image of the decomposed image are selected as feature images. Three selected feature images are synthesized of a single feature image, and a region including the plate is segmented by the correlation coefficient between the feature image and the synthesized image. For segmented plate region, the car plate region is extracted by deciding the Y-axis region composed by vertical region, the car plate region is extracted by deciding the Y-axis region composed by vertical histogram and the X-axis region composed by the variance histogram. Some experiment results of the various image and shown. It has been shown from the results with the high rate of 96% that the car number plates can be segmented and extracted more extractly and efficiently than converntional method.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3683-3693
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• 1999

Most recognition methods of car licence plate have difficulties concerning plate recognition rates and system stability in that restricted car images are used and good image capture environment is required. To overcome these difficulties, I proposed a new recognition method of car licence plates, in which both intensity variation and color information are used. For a captured car image, multiple candidate plate-bands are extracted based on the number of intensity variation. To have an equal performance on abnormally dark and bright Images. plate lightness is calculated and adjusted based on the brightness of plate background. Candidate plate regions are extracted using contour following on plate color pixels in oath plate band. A candidate region is decided as a real plate region after extracting character regions and then recognizing them. I recognize characters using template matching since total number of possible characters is small and they art machine printed. To show the efficiency of the proposed method, I tested it on 200 car images and found that the method shows good performance.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.173-179
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• 2012

In this paper, we presents a new method for vehicle number plate detection. Our method is basically the method extracting a rectangles from a car image because the shape of a vehicle number plate is a rectangle. For detecting the vehicle number plate, firstly, the contrast of the input image is enhanced. Then, the lines in the image are obtained by using LSD(line segment detector), and rectangles in the image are detected from the line data. These rectangles are the candidates of the car plate, from which the car plate is selected. In this procedure, the method of detecting rectangles is our proposed method, which consists of three stages: (1) extracting corners from the line segments by LSD; (2) extracting diagonal lines from the corner data; and (3) detecting rectangles from diagonal line information. And finally the vehicle number plate is selected from these rectangles by using the feature of the vehicle number plate and the inside information of rectangles. In the experiments with the 100 images captured by our digital camera, we have achieved a detection rate of 94%.