• 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 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 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 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 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 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.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.3
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• pp.1-14
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• 2009

In this paper, we propose a feature vector and its applying method which can be utilized for the extraction of the car license plate region. The proposed feature vector is extracted from direction code histogram of edge direction of gradient vector of image. The feature vector extracted is forwarded to the MLP classifier which identifies character and garbage and then the recognition of the numeral and the location of the license plate region are performed. The experimental results show that the proposed methods are properly applied to the identification of character and garbage, the rough location of license plate, and the recognition of numeral in license plate region.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.21-32
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• 2006

The license plate recognition (LPR) unit consists of the following core components: plate region segmentation, individual character extraction, and character recognition. Out of the above three components, accuracy in the performance of plate region segmentation determines the overall recognition rate of the LPR unit. This paper proposes an algorithm for segmenting the license plate region on the front or rear of a vehicle in a fast and accurate manner. In the case of the proposed algorithm images are captured on the spot where unmanned monitoring of illegal parking and stowage is performed with a variety of roadway environments taken into account. As a means of enhancing the segmentation performance of the on-the-spot-captured images of license plate regions, the proposed algorithm uses a mathematical model for license plate colors to convert color images into digital data. In addition, this algorithm uses Gaussian smoothing and double threshold to eliminate image noises, one-pass boundary tracing to do region labeling, and MBR to determine license plate region candidates and extract individual characters from the determined license plate region candidates, thereby segmenting the license plate region on the front or rear of a vehicle through a verification process. This study contributed to addressing the inability of conventional techniques to segment the license plate region on the front or rear of a vehicle where the frame of the license plate is damaged, through processing images in a real-time manner, thereby allowing for the practical application of the proposed algorithm.

• 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.