• 전기전자학회논문지
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• 제15권4호
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• pp.274-280
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• 2011

본 연구에서는 SoC를 이용한 임베디드 시스템에 리눅스 OS 환경을 구축하고 번호판 인식 시스템을 구현하여 그 성능을 측정하였다. 자동차 번호판을 인식하기 위해서는 번호판을 검출하고 검출된 번호판을 보정 한 뒤 각 문자들에 대해 인식을 한다. 번호판 검출 방법으로는 레이블링 기법과 숫자의 특징을 이용하여 검출하였다. 검출된 번호판의 표기되어 있는 숫자들은 각각의 좌표가 있다. 이러한 숫자들의 좌표를 비교하여 영상을 보정하고 템플릿 매칭을 통해 인식을 한다. 그 결과로 번호판의 검출율은 96%, 문자 인식률은 73%, 숫자 인식률은 97%로 나타났다. 인식 시스템은 기존의 PC기반이 아닌 임베디드 보드에서 측정 되었으며 총 인식시간은 약 0.66초가 소요되었다.

• 반도체디스플레이기술학회지
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• 제13권2호
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• pp.7-12
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• 2014

A vehicle license plate includes the most important information for recognition and classification of the vehicle. In this paper, we propose a vehicle license plate recognition system using image binarization and template matching. In the proposed system, an image of the vehicle license plate is converted into a gray scale image and the gray image undergoes the binarization process. Finally, the numbers on the plate are extracted from the binary image using the template matching algorithm.

• 한국멀티미디어학회논문지
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• 제25권4호
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• pp.568-574
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• 2022

In this paper, direction normalization and CNN deep learning are used to develop a more reliable license plate recognition system. The existing license plate recognition system consists of three main modules: license plate detection module, character segmentation module, and character recognition module. The proposed system minimizes recognition error by adding a direction normalization module when a detected license plate is inclined. Experimental results show the superiority of the proposed method in comparison to the previous system.

• 산업공학
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• 제14권3호
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• pp.296-301
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• 2001

The focus of this study is to redesign the Korean license plate by comparing the USA's plate with European plate formed by 1-line system in order to increase a read rate of Korea license plate. And we have compared the read rate of the new design license plate with that of the present license plate and that of the license plate studied so far. As an experimental method (used in a precedent research), we use three kinds of methods that are the measurement of the read-distance, the measurement reading-rate under the short-term exposure and the measurement of the reading-rate when driving. First, three kinds of measurements for plates of five nations are performed. Then we redesign the new Korean license plate under the base of read rates obtained by five nation's plate. As alternatives, we choose five license plates. Those alternatives are the redesigned license plate, the present license plate, the license plate studied so far, and two types of eternity license plates made by the Korea Transport Institute. When we compare results of the read-distance, there is no significant in term of different the read-distance between the alternatives. But there is a significant difference in term of the misreading-rate and the read-rate when diving. Therefore, it is necessary to redesign the present license plates because of a high misreading-rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권10호
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• pp.3490-3507
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• 2014

License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.

• 전기학회논문지
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• 제65권9호
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• pp.1531-1540
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• 2016

In this paper, we propose vehicle license plate recognition system based on Radial Basis Function Neural Networks (RBFNNs) with the use of local binarization functions and canny edge algorithm. In order to detect the area of license plate and also recognize license plate numbers, binary images are generated by using local binarization methods, which consider local brightness, and canny edge detection. The generated binary images provide information related to the size and the position of license plate. Additionally, image warping is used to compensate the distortion of images obtained from the side. After extracting license plate numbers, the dimensionality of number images is reduced through Principal Component Analysis (PCA) and is used as input variables to RBFNNs. Particle Swarm Optimization (PSO) algorithm is used to optimize a number of essential parameters needed to improve the accuracy of RBFNNs. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. Image data sets are obtained by changing the distance between stationary vehicle and camera and then used to evaluate the performance of the proposed system.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2002년도 춘계학술대회논문집
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• pp.183-187
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• 2002

JLBS는 multi-server multi-queue 방식을 채택하고 있으며, 각 tool별로 할당된 license 수에 비례하여 system을 할당한다. 그러나 이러한 방식의 문제점은 특정 tool에만 job이 집중될 경우 비효율적이다. 즉 특정 system은 모두 사용되고 있고, queue에는 많은 job이 대기하고, 여타 tool에 해당하는 queue에는 대기하는 job이 없어서 system이 그냥 놀고 있는 현상이 발생한다. 본 논문에서는 이러한 단점을 극복하기 위해 각 tool에 license수에 비례하여 할당되었던 system을 모든 tool들이 system을 공유할 수 있도록 하는 방법을 제안했다. 대신 system의 숫자는 줄이고, license의 숫자는 더 할당하는 방법으로, 기존의 방법보다 더 효율적으로 나타났다.

• 한국컴퓨터정보학회논문지
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• 제21권3호
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• pp.33-38
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• 2016

Research on the development of key technologies of social work protection and content mashup tools has been carried out as an R&D project granted by the Korea Copyright Commission from 2013. The research aims to provide efficiency of the production environment of the secondary work of the digital contents as well as a systematic solution to the regulation-related problems. The essential features of the distribution management system for cooperative works developed though this study are the decision of the selling prices reflecting various license fee factors and the transparent distribution of the license fees. This paper represents a model which can automatically calculate the amount of the license fee in each derivative stage, independently of the license fee policies on each of the subsidiary contents when N-th works are producted on the basis of a previously approved first work.

• 한국지능시스템학회논문지
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• 제20권2호
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• pp.262-266
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• 2010

Recognizing a license plate of a vehicle has widely been issued. In this thesis, firstly, mean shift algorithm is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. We then present an approach to recognize a vehicle's license plate using the Fuzzy ARTMAP neural network, a relatively new architecture of the neural network family. We show that the proposed system is well to recognize the license plate and shows some compute simulations.

• 융합신호처리학회논문지
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• 제2권1호
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• pp.1-11
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• 2001

자동차 번호판은 조명과 카메라에 따라 영상에서 다양한 형태로 나타나고 영상내의 잡음으로 인해 알고리즘 방식으로 자동차 번호판을 인식하기가 쉽지 않다. 이러한 문제에 적합한 해결 방법으로 본 논문에서는 학습 기반의 자동차 번호판 인식 시스템을 제안한다. 제안한 시스템은 자동차 검출 모듈, 번호판 추출 모듈, 번호판 문자 인식 모듈로 구성된다 본 논문에서는 자동차 번호판 추출을 위해서 입력 영상의 잡음에 상대적인 영향이 적은 시간-지연 신경망(Time-Delay Neural Networks : TDNN)과 번호판 인식을 위해서 일반적인 신경망보다 일반화 성능이 뛰어난 서포트 벡터 머신(Support Vector Machines : SVMs)을 시스템에 적용한다. 주차장과 톨게이트에서 여러 시간대의 움직이는 자동차 영상들을 실험한 결과, 번호판 추출율은 97.5%, 번호판 문자 인식률은 97.2%의 성능을 내었고, 전체 시스템 성능은 947%이며 처리 시간은 약 1조 미만이다. 따라서 본 논문에서 제안한 시스템은 실세계에서 유용하게 적용될 수 있다.