• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1114-1125
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• 2013

License Plate Detection (LPD) is a key component in automatic license plate recognition system. Despite the success of License Plate Recognition (LPR) methods in the past decades, the problem is quite a challenge due to the diversity of plate formats and multiform outdoor illumination conditions during image acquisition. This paper aims at automatical detection of car license plates via image processing techniques. In this paper, we proposed a real-time and robust method for license plate detection using Heuristic Energy Map(HEM). In the vehicle image, the region of license plate contains many components or edges. We obtain the edge energy values of an image by using the box filter and search for the license plate region with high energy values. Using this energy value information or Heuristic Energy Map(HEM), we can easily detect the license plate region from vehicle image with a very high possibilities. The proposed method consists two main steps: Region of Interest (ROI) Detection and License Plate Detection. This method has better performance in speed and accuracy than the most of existing methods used for license plate detection. The proposed method can detect a license plate within 130 milliseconds and its detection rate is 99.2% on a 3.10-GHz Intel Core i3-2100(with 4.00 GB of RAM) personal computer.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.103-113
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• 2019

License plate recognition camera is dedicated device designed for acquiring images of the target vehicle for recognizing letters and numbers in a license plate. Mostly, it is used as a part of the system combined with server and image analysis module rather than as a single use. However, building a system for vehicle license plate recognition is costly because it is required to construct a facility with a server providing the management and analysis of the captured images and an image analysis module providing the extraction of numbers and characters and recognition of the vehicle's plate. In this study, we would like to develop an embedded type convergent camera (Edge Base) which can expand the function of the camera to not only the license plate recognition but also the security CCTV function together and to perform two functions within the camera. This embedded type convergence camera equipped with a high resolution 4K IP camera for clear image acquisition and fast data transmission extracted license plate area by applying YOLO, a deep learning software for multi object recognition based on open source neural network algorithm and detected number and characters of the plate and verified the detection accuracy and recognition accuracy and confirmed that this camera can perform CCTV security function and vehicle number plate recognition function successfully.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.1-8
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• 2010

Recently, the character image processing technology using portable device camera image at home and abroad are actively conducted, but Practical use are lower rate because of accuracy and time-consuming process problems. In this paper, we propose the license plate recognition method based on geometric information from portable device camera image. In the extracted license plate region we recognize characters using the chain code and the Thickness information through the cumulative projected edge after performing the pre-processing work considering the angle difference, the contrast enhancement and the low resolution from portable device camera image. The proposed algorithm is effective and accurate recognition by light and reducing the processing time. And, the results from the character recognition success rate was 95%. In the future, we will research about license plate recognition algorithm using long distance image or added motion blur image.

• The Journal of the Korea Contents Association
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• v.13 no.10
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• pp.19-26
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• 2013

In the Malaysian license plates, alphabets and numerals which are made by plastic, are adhered to a frame as embossing style and occasionally characters in horizontal, vertical directions are aligned with narrow space. So the extraction of character stroke information can be hard in the vehicle images of low illumination intensity. In this paper, Malaysian license plate recognition algorithm for low illumination intensity image is proposed. DoG filtering based character stroke generation method is introduced to derive exact connected components of strokes in the vehicle image of low illumination intensity. After localization of plate by connected component analysis, characters are segmented and recognized. Algorithm is experimented for the 6,046 vehicle images captured in Kuala Lumpur by IR camera without using any special light during day and night. The experimental results show that recognition accuracy of plates is 96.1%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.349-352
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• 2006

To cope with traffic congestion, traffic accidents and lack of parking facilities, caused by dramatic increase in total vehicle number, vigorous researches on managing vehicles efficiently are done, both domestically and internationally. The vehicle license plate recognition makes effective management of traffic possible, with its wide application in many fields, covering from speed enforcement, collecting toll, stolen vehicle detection to parking management. The vehicle license plate recognition system causes high cost for collecting training data. Many researches are done by using the virtual sample method, which can be effective for utilizing limited number of training data by generating virtual sample. This paper investigates techniques to improve the performance of vehicle license plate recognition by using the training data's annexation. Also, popular methods for virtual sample creation used for text recognition algorithm are analyzed and their effectiveness is verified.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1496-1509
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• 2005

In this paper, an adaptive license plate recognition system which detects and recognizes license plate at real-time by using projected plane convolution and Decision Tree Classifier is proposed. And it was tested in circumstances which presence of complex background. Generally, in expressway tollgate or gateway of parking lots, it is very difficult to detect and segment license plate because of size, entry angle and noisy problem of vehicles due to CCD camera and road environment. In the proposed algorithm, we suggested to extract license plate candidate region after going through image acquisition process with inputted real-time image, and then to compensate license size as well as gradient of vehicle with change of vehicle entry position. The proposed algorithm can exactly detect license plate using accumulated edge, projected convolution and chain code labeling method. And it also segments letter of license plate using adaptive binary method. And then, it recognizes license plate letter by applying hybrid pattern vector method. Experimental results show that the proposed algorithm can recognize the front and rear direction license plate at real-time in the presence of complex background environments. Accordingly license plate detection rate displayed $98.8\%$ and $96.5\%$ successive rate respectively. And also, from the segmented letters, it shows $97.3\%$ and $96\%$ successive recognition rate respectively.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.507-509
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• 1997

One of the most difficult tasks in the process of car license plate is the extraction of each character from within license plate region. This paper presents a real-time recognition of car licence number using neural network in parking lot. The feature parameters of letters and numbers of license plate are extracted by thinning algorithm. Both feature parameters are used to train neural networks for the image recognition.

• 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.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.1002-1005
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• 1995

This paper presents an enhanced preprocessing and recognition algorithm for automatic vehicle license plate recognition system. The algorithm first applies horizontal gradient filter followed by thresholding and mathematical morphology operation for preprocessing. The final stage of the preprocessing is the application of connected component analysis in order to estimate the license plate region. For the recognition of the serial numbers of the plates, we developed a very effective algorithm. We call this zerocrossing count algorithm. This paper presents a detail of this algorithm and compare the performance with a template matching algorithm which utilizes correlation coefficient.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1583-1584
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• 2007

To test the performance of the proposed algorithm, images of seventy vehicle were tested. The success rates for license plate and character recognition were approximately 98.4% and 96.3%, respectively.