• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.261-268
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• 2007

In this paper, We propose a new technique extract efficiently a car number plate based on edge projection. In order to obtain the region of car number plate, we use a motive that the luminance differences between the number plate background and characters. And, we introduce a projection technique to obtain character parts based on edge image. In vertical direction. we propose a shape matching method. Specially the new number plate standard has more characters than the old one in horizontal direction and, it is efficiently used to extract the number plate. Therefore, the proposed technique is useful to the new number plate standard. In simulation results. We have illustrated that our algorithm can recognize different number plates with a success ration of 90%.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1515-1524
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• 2008

This paper proposes a method that detects a license plate in complex background using a multiple-color model and character layout information. A layout of a green license plate is different from that of a white license plate. So, this study used a strategy that firstly assumes the plate color and then utilizes its layout information. At first, it extracts green areas from an input image using a multiple-color model which combined HIS and YIQ color models with RGB color model. If green areas are detected, it searches the character layout of the green plate by analyzing the connected components in each areas. If not detected, it searches the character layout of the white plate in all area. Finally, it extracts a license plate by grouping the connected components which corresponds to characters. Experimental result shows that 98.1% of 419 input images are correctly detected. It also shows that the proposed method is robust against illumination, shadow, and weather condition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1352-1357
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• 2002

This Paper describes algorithm to extract license plates in vehicle images. Conventional methods perform preprocessing on the entire vehicle image to produce the edge image and binarize it. Hough transform is applied to the binary image to find horizontal and vertical lines, and the license plate area is extracted using the characteristics of license plates. Problems with this approach are that real-time processing is not feasible due to long processing time and that the license plate area is not extracted when lighting is irregular such as at night or when the plate boundary does not show up in the image. This research uses the gray level transition characteristics of license plates to verify the digit area by examining the digit width and the level difference between the background area the digit area, and then extracts the plate area by testing the distance between the verified digits. This research solves the problem of failure in extracting the license plates due to degraded plate boundary as in the conventional methods and resolves the problem of the time requirement by processing the real time such that practical application is possible. This paper Presents a power automated license plate recognition system, which is able to read license numbers of cars, even under circumstances, which are far from ideal. In a real-life test, the percentage of rejected plates wan 13%, whereas 0.4% of the plates were misclassified. Suggestions for further improvements are given.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.796-798
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• 2004

기존의 번호판 인식 시스템에서는 대부분 카메라가 고정 상태에서 차량의 전면부를 찍어 영상을 획득하고, 이로부터 번호판을 추출하고 인식한다 그러나 본 연구에서는 기존 연구들과 달리 이동 중인 자동차에 카메라를 설치하여 움직이는 자동차의 영상을 획득하여 번호판을 추출하고 인식한다. 인식하고자 하는 영상이 잡음이나 왜곡 없이 깨끗하다면 인식 과정은 간단하게 수행될 것이다. 그러나, 실제로 얻어진 영상은 간단한 방법으로 인식하기에는 어려올 정도로 왜곡이나 변형이 심한 경우가 많다. 따라서 본 논문에서는 SVM 전단에 모듈라 신경망을 결합하여 인식하는 방법을 사용함으로써 잡음과 같은 변형에 덜 민감하도록 하고자 하였다. 실험결과, 제안하는 분류기를 이용한 방법이 번호판 인식에 우수한 성능을 보임을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.833-838
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• 2003

This paper presents novel methods of recognizing license plates of passing vehicles outdo(n. In particular, the proposed method is much robust for inclined plates caused by the changes of camera placement. To acquire fine images of quickly passing vehicles under a wide range of illumination conditions, we developed a sensing system having superb characteristics. We expanded the dynamic range and eliminated the blurring of images of fast moving vehicles by synthesizing a pair of synchronized images with different intensities. furthermore, to extend the flexibility of the positioning of the TV camera, we propose a recognition algorithm that can be applied to inclined plates. The performance of the integrated system was investigated on real images of vehicles captured under various illumination conditions. The recognition rates of over 99% (conventional plates) and over 97% (highly inclined plates) shows that the developed system is effective for license plate recognition.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.896-901
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• 2000

차량 번호판 인식 시스템은 크게 번호판 영역의 추출과 인식 단계로 구분된다. 본 논문에서는 전처리단계로써 임계화 방식을 이용하여 번호판 영역을 추출한다. 차량 영상을 임계화하고 영상에서 발생되는 잡음을 제거한다. 잡음이 제거된 차량 영상에서 각 라인의 밀도비율을 계산하여 번호판 영역에서 나타나는 밀도의 비율과 비슷하게 나타나는 영역을 후보영역으로 설정한다. 설정된 후보영역이 번호판 영역의 특징과 유사하게 나타나는 부분을 추출한다. 그리고 추출된 번호판 영역은 코호넨 알고리즘의 2${\times0}$2마스크에 적용시켜서 윤곽선을 추출하고, 번호판의 문자와 숫자를 인식한다. 코호넨 알고리즘의 2${\times0}$2마스크를 이용하게 되면, 윤곽선의 잡음을 최대한으로 줄여주는 특성을 가진다. 잡음이 제거된 후에, 번호판의 문자와 숫자들을 코호넨 알고리즘을 이용하여 인식하였다. 실험 결과에서는 임계화 작업을 이용한 번호판 추출과 코호넨 알고리즘을 이용한 번호판 인식이 우수하는 것을 알 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.111-112
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• 2013

본 논문에서는 최근 증가하고 있는 차량의 질서 위반 행위 및 과태료 체납, 대포차 등에 대한 문제를 보다 효율적으로 해결하기 위하여 이동이 간편한 스마트 기기를 통하여 차량의 번호판을 인식하고 관리할 수 있는 시스템을 제시하고자 한다. 영상 촬영 기능과 데이터 전송 기능을 가지고 있는 휴대용 단말기를 소지한 사용자가 자동차의 번호판을 촬영했을 시 자동차 번호판을 인식하고 촬영된 영상과 차량 번호에 대한 데이터를 원격지 서버로 전송하여 차량 소유자에 대한 과태료 체납여부, 위반행위 등을 조회하여 해당 차량에 대한 정보 등을 신속하게 확인하고 현장에서 복합적으로 대응할 수 있는 시스템을 개발하고자 한다. 본 논문에서 제시할 주요한 기능은 영상처리를 통한 차량 번호판 인식과 인식된 차량 번호 정보와 번호판 영상 정보를 원격지 서버로 전송하여 정보를 관리 할 수 있는 시스템을 제시하고자 한다.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.2
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• pp.32-43
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• 2004

In this paper, a system which can effectively recognize the plate image extracted from camera set on a moving car is proposed. To extract car licence plate from moving vehicles, multiple candidates are maintained based on the strong vertical edges which are found in the region of car licence plate. A candidate region is selected among them based on the ratio of background and characters. We also make a comparative study of recognition performance between support vector machines and modular neural networks. The experimental results lead us to the conclusion that the former is superior to the latter. For a better recognition rate, a simple method combining the support vector machine with modular neural network where the output of the latter is used as the input of the former is suggested and evaluated. As we expected, the hybrid one shows the best result among those three methods we have mentioned.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.776-788
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• 2020

In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.

• Journal of KIISE
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• v.43 no.2
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• pp.186-195
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• 2016

This paper proposes a vehicle license plate detection method in real road environments using 8 bit-MCT features and a landmark-based Adaboost method. The proposed method allows identification of the potential license plate region, and generates a saliency map that presents the license plate's location probability based on the Adaboost classification score. The candidate regions whose scores are higher than the given threshold are chosen from the saliency map. Each candidate region is adjusted by the local image variance and verified by the SVM and the histograms of the 8bit-MCT features. The proposed method achieves a detection accuracy of 85% from various road images in Korea and Europe.