• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.31-37
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• 2001

License plate recognition system for an exclusive bus-lane is made of 5 core parts which are vehicle detection, image acquisition individual character extraction, character recognition and data transmission. Among them, the accuracy of license plate extraction can bring effect significantly to the accuracy of a whole system recognition rate also the more exact extraction of license plate is required in various weather and environment conditions. Therefore in this paper we propose a plat extraction algorithm that makes pyramid structure to reduced the extraction processing time binarizes plate's template region using adaptive thresholding extracts candidate region containing plate, and verifies a final region using plate character distribution characteristics among the candidates. Experimenal results were exactly extracted the license plate region by using proposed method to the image obtained in an exclusive bus-lane with various weather and environment conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.408-410
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• 2014

In this paper a license plate area extraction of labeling the vehicle images is proposed. Studies on license plate recognition systems have largely been conducted and there is a tendency of increasing license plate recognition rates. In this paper a license plate region is extracted from an image labeling for the region of interest and research on technology for labeling sample image using the Otsu algorithm to binary.

• Journal of Navigation and Port Research
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• v.28 no.8
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• pp.727-733
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• 2004

In the car license plate recognition system, it is very important to extract the part of the license plate from the car image. In this paper, I use ACL algorithm to extract the license plate image from car image. The ACL algorithm is used to color and luminance information, either. Therefore in this paper, suggested algorithm is called ACL algorithm The ACL algorithm uses color, luminance information and the rate of license plate information Each of these information are used to exact area of license plate. The result of experiment to extract the car license plate with ACL algorithm is 97% extraction rate. The result of experiment with ACL algorithm for the character region, character recognition is 92% extraction rate.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.218-222
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• 2003

A technology that recognize the car license plate have accomplished a lot of developments for latest several years. Key technology for correct recognition is correct abstraction of plate area. Existent studies have used horizontal/vertical edge, some geometrical characteristics of license plate, and the color information. But, in case of extracting a plate using above characteristics, correct extraction of a license plate inclined by sight which see license plate is difficult. Therefore, this paper is propose new method that correctly extract license plate using the color information and linear regression method.

• 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.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1172-1179
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• 2015

A wireless license plate region extracting module is proposed for LPR system controlling multiple gates. This module is cheaply implemented using Raspberry Pi which is open source and high performance. First, as the upper 1/3 of the captured image is discarded as it has no useful information on license plate. Using the OpenCV libraries the edge image is got by Canny algorithm after applying Gaussian filtering to gray image, and the labeling is conducted for 4 consecutive numbers in license plate. These numbers are located using various decision equations, and expanding the numbers region the final license plate region can be extracted. The result image is transferred to Server using wifi direct. Using the proposed module it becomes easy to set up and maintain the LPR system. The experimental results showed that the successful extracting rate was 98.4% using 500 car images with 640 × 480 resolution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.80-87
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• 2004

In this paper we proposed the novel feature extraction method that is able to extract the individual characters from the license plate area of the car image more precisely by using the RGB color information and the fuzzy binarization newly proposed. The proposed method, first, extracts from the original image the areas that the pixels with the colors around the green are concentrated on as the candidate areas of the license plate, and selects the area with the most intensive distribution of pixels with the white color among the candidate areas as the license plate area. Second the noises of the license plate area should be removed by using 34{\times}$3 Sobel masking, and the fuzzy binarization method are proposed and applied to the license plate area to generate the binarized image of the license plate area. Lastly, the application of the contour tracking algorithm to the binarized area extracts the individual characters from the license plate area. The experiment on a variety of the real car images showed that the proposed method generates the higher rate of success for character extraction than the previous methods.

• 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.

• 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 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.