• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.125-132
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• 2006

The existing recognition system of license plates cannot get the satisfactory result in noise environments. The purpose of this paper is to propose an algorithm that can recognize the region of license plates accurately in a noise environment. The algorithm is formulated by reorganizing the U- and V-channels of YUV color space as YUV is insensitive to light and carries less data than RGB color information. The region of license plates has been extracted by the geometric characteristics, sizes, and places of labeling images. The proposed algorithm was found to improve the process of extracting the region of license plates in various noise environments.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1081-1084
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• 1999

This paper proposes a method of extracting automobile license plate information using color image processing and separation of character regions. The hue and saturation of color information is need for license plate extraction and the specified standard location ratio is need for character region separation. Simulation results show that the proposed algorithm can detect license plates and separate character regions successfully.

• Journal of Digital Contents Society
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• v.10 no.4
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• pp.597-604
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• 2009

For the improvement of license plate recognition system, correct extraction of a license plate region as well as character recognition is important. In this paper, with the analysis and classification of the error patterns in the process of plate region extraction, we tried to improve the extraction of the region using HoG(histogram of gradient) features and Adaboost. The results show that the HoG feature is robust to the noise and various types of the plates, and also is very effective to extract the region failed before.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1256-1259
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• 2002

In this paper, we have proposed license plate recognition system for multi-national vehicle license plate using geometric features along with hybrid and seven segment pattern vectors. In the proposed system, we suggested to find horizontal and vertical relation after going through preparation process with inputted real-time license plate image of Korea and Japan, and then to classify license plate with using characteristic and geometric information of license plates. It classifies the extracted license plate images into letters and numbers, such as local name, local number, classification character and license consecutive numbers, and recognize license plate of Korea and Japan by applying hybrid and seven segments pattern vectors to classified letter and number region. License plate extraction step of the proposed system uses width and length information along with relative rate of Korean and Japanese license plate. Moreover, it exactly segmentation by letters with using each letter and number position information within license plate region, and recognizes Korean and Japanese license plates by applying hybrid and seven segment pattern vectors, containing characteristics related to letter size and movement within segmented letter area. As the result of testing the proposed system in real experiment, it recognized regardless of external lighting conditions as well as classifying license plates by nations, Korea and Japan. We have developed a system, recognizing regardless of inputted structural character of vehicle licenses and external environment.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1291-1294
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• 2003

This paper presents an approach to extract license plates from parking regulation images which is captured in various photographing direction and complex background. first, we search each row at regular intervals starting from the bottom of a license-plate image, and we set up a rough region for a certain zone in which the sign of intensity vector changes frequently enough and color of license plate is detected enough, assuming it as a candidate location of a license plate. And then, we extract an elaborate area of a license plate by horizontally and vertically projecting vertical edges. Here, tar types of the private and the public, are easily classified according to the color of extracted plates. To evaluate proposed method, we used 200 actual regulation images. As a result, the proposed method showed extraction rate of 96%, which is 9% higher than the previous method using only intensity vector.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1495-1504
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• 2008

New license plates have been used since 2007. This paper proposes a new license plate extraction method using a gray labeling and a fuzzy reasoning method. First, the proposed method extracts the candidate plates by the gray labeling which is the enhanced version of a non-recursive flood-filling algorithm. By newly designed fuzzy inference system. fitness of each candidate plates are calculated. Finally, the area of the license plate in a image is extracted as a region of the candidate label which has the highest fitness. In the experiments, various license plate images took from indoor/outdoor parking lot, street, etc. by digital camera or cellular phone were used and the proposed extraction method was showed remarkable results of a 94 percent success.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.648-651
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• 2003

In this paper, we propose the recognition system of a license plate using SOM algorithm. The recognition of license plate was investigated by means of the SOM algorithm. The morphological information of horizontal and vertical edges was used to extract a plate region from a car image. In addition, the 4-direction contour tracking algorithm was applied to extract the specific area, which includes characters from an extracted plate area. Therefore, we proposed how to extract license plate region using morphological information and how to recognize the character string using SOM algorithm. In this paper, 50 car images were tested. The extraction rate obtained by the proposed extraction method showed better results than that from the color information of RGB and HSI, respectively. And the license plate recognition using SOM algorithm was very efficient.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3188-3207
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• 2017

In this paper, we propose an image enhancement algorithm to improve license plate extraction rate in various environments (Day Street, Night Street, Underground parking lot, etc.). The proposed algorithm is composed of image enhancement algorithm and license plate extraction algorithm. The image enhancement method can improve an image quality of the degraded image, which utilizes a histogram information and overall gray level distribution of an image. The proposed algorithm employs an interpolated probability distribution value (PDV) in order to control a sudden change in image brightness. Probability distribution value can be calculated using cumulative distribution function (CDF) and probability density function (PDF) of the captured image, whose values are achieved by brightness distribution of the captured image. Also, by adjusting the image enhancement factor of each part region based on image pixel information, it provides a function that can adjust the gradation of the image in more details. This processed gray image is converted into a binary image, which fuses narrow breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour by using morphology operations. Then license plate region is detected based on aspect ratio and license plate size of the bound box drawn on connected license plate areas. The images have been captured by using a video camera or a personal image recorder installed in front of the cars. The captured images have included several license plates on multilane roads. Simulation has been executed using OpenCV and MATLAB. The results show that the extraction success rate is more improved than the conventional algorithms.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.965-967
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• 1995

One of the most difficult tasks in the process of automatic vehicle license plate recognition is the extraction of each character from within license plate region. In many cases, characters, especially serial numbers of plates are connected together due to noise and plate accessories. The recognition process may not be successful without extracting these characters effectively. This paper presents an algorithm to extract these connected characters very effectively. The algorithm utilizes mathematical morphology, connected component analysis, and gradient filters for character extraction. The paper also presents thorough experimental results as well as details of the algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.47-55
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• 2003

In this paper, we propose a new approach to detect peculiar features of license plates using intensity vector and composite color component in order to extract license plates from parking regulation images, which is captured in various locations around the front or the rear of cars at various times and places, and in which complex background is included. We fundamentally use both features that intensity value repeats frequently increasing and decreasing because intensity is obviously different at numerics and background, and that color is uniform in the area of license plates. First, we search each row at regular intervals starting from the bottom of a license-plate image, and we set up a rough region for a certain zone in which tile sign of intensity vector changes frequently enough and color of license plate is detected enough, assuming it as a candidate location of a license plate. And then, we extract an elaborate area of a license plate by projecting vertical edges horizontally and vertically. Here, type of cars, such as the urinate and the public, is easily classified according to the color of extracted plates. We used 200 actual regulation images, which are captured at various times and places, to evaluate the performance of the proposed method. As a result, the proposed method showed extraction rate of 96%, which is 9% higher than the previous method using only intensity vector.