• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.339-345
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• 2002

Extracting of car licens plate os important for identifying the car. Since there are some problems such as poor ambient lighting problem, bad weather problem and so on, the car images are distorted and the car license plate is difficult to be extracted. This paper proposes a method of extracting car license plate using motor vehicle regulation. In this method, some features of car license plate according to motor vehicle regulation such as color information, shape are applied to determine the candidate of car license plates. For the result of recognition by neural network, the candidate which has characters and numbers patterns according to motor vehicle regulation is certified as license-plate region. The results of the experiments with 70 samples of real car images shoe the performance of car license-plate extraction by 84.29%, and the recognition rate is 80.81%.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.447-450
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• 2001

자동차 번호판 인식 시스템은 크게 세 부분으로 나뉘어 질 수 있는데 그 첫 부분이 Camera를 통해서 획득된 영상에서 번호판 영역을 추출하는 것이다. 본 논문에서는 자가용과 영업용 번호판의 배경이 모두 다른 부분과 차이를 가지고 있다는 점을 이용하여 번호판 영역 추출을 위하여 기존의 방법과 달리 Color 정보를 이용하였다. Edge 검출이나 Gray level의 변화값을 이용하지 않고 Color 정보를 이용함으로써 번호판이 구부러진 영상이나 Noise를 통해서 훼손된 영상, Contrast가 낮은 영상에도 영역 추출에 강한 성능을 나타내었다. Camera를 통해서 획득된 RGB 영상을 YCbCr Format으로 바꾸고 그 중 Cb와 Cr 정보를 이용하여 번호판 영역을 검출하고 인증과정을 거쳐서 추출된 영상이 실제로 번호판 영상인지를 확인하는 단계를 거쳤다. 실험을 통하여 주간, 야간 및 훼손되거나 Noise가 많이 발생한 영상에서도 강한 성능을 나타냄을 볼 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.596-599
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• 2001

Extracting of car licens plate is important for identifying the car. Since there are some problems such as poor ambient lighting problem, bad weather problem and so on, the car images we distorted and the tar license plate is difficult to be extracted. This paper proposes a method of extracting car license plate using motor vehicle regulation. In this method, some features of car license plate according to motor vehicle regulation such as color information, shape are applied to determine the candidate of car license plates. For the result of recognition by neural network, the candidate which has characters and numbers patterns according to motor vehicle regulation is certified as license-plate region. The results of the experiments with 70 samples of real car images shoe the performance of car license-plate extraction by 84.29%, and the recognition rate is 80.81%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.550-554
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• 2005

본 논문에서는 블럽을 사용해서 다수의 자동차 후면의 번호판을 추출하는 방법을 제안한다. 입력 영상에서 번호판의 문자와 배경사이의 명암도 차이를 이용하여, 입력 영상의 모든 블럽을 찾고, 찾아낸 블럽을 둘러싸는 최소의 사각형들을 구한다. 이 사각형들 중에서 일련의 경향성을 갖는 블럽 그룹을 찾는다. 찾아난 블럽 그룹이 자동차 번호판인지 아닌지를 SVM을 이용하여 확인한다. 적응적 이진화를 제외한 전처리작업을 하지 않았음에도 불구하고 번호판 검출률은 매우 높았으며, 번호판을 검출하는데 걸리는 시간도 길지 않았다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.1050-1056
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• 2000

In this paper, We Propose the automobile license plate extraction method using Discrete Cosin Transform and Correlation fem automobile image obtained through digital camera. The automobile license plate is consisted of the character and rectangle background of it. We extracted the automobile edge image by the DCT processing of automobile image and Obtained the automobile license plate from the automobile edge image by Correlation processing. We separated characters from automobile license plate using the projection histogram. Compare to the previous methods, we obtained the good result from extracting the automobile license plate at night, very strong light and bad weather.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.1-11
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• 2001

This paper presents a learning-based approach for the construction of license Plate recognition system. The system consist of three modules. They are respectively, car detection module, license plate recognition module and recognition module. Car detection module detects a car in the given image sequence obtained from the camera with simple color-based approach. Segmentation module extracts the license plate in detect car image using neural network as filters for analyzing the color and texture properties of license plate. Recognition module then reads characters in detected license plate with support vector machine (SVM)-based characters recognizer. The system has been tested from parking lot and tollgate, etc. and have show the following performances on average: Car detect rate 100%, segmentation rate 97.5%, and character recognition rate about 97.2%. Overall system performances is 94.7% and processing time is one sec. Then our propose system does well using real world.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1686-1692
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• 1999

This paper describes the feature area-based vehicle plate recognition system(VPRS). For the extraction of vehicle plate in a vehicle image, we used the method which extracts vehicle plate area from a s vehicle image using intensity variation. For the extraction of the feature area containing character from the extracted vehicle plate, we used the histogram-based approach and the relative location information of individual characters in the extracted vehicle plate. The extracted feature area is used as the input vector of ART2 neural network. The proposed method simplifies the existing complex preprocessing the solves the problem of distortion and noise in the binarization process. In the difficult cases of character extraction by binarization process of previous method, our method efficiently extracts characters regions and recognizes it.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.623-626
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• 2003

본 논문에서는 현재 자동차 번호판의 형식이 구 번호판과 신 번호판 두 가지 유형으로 구성되어 있다는 점을 고려하여 번호판의 세부적 세그멘테이션의 성능을 개선하는 방법에 대하여 제시한다. 컴퓨터 비젼을 바탕으로 한 자동차 번호판의 인식방법과 문자인식방법은 비용면이나 간편성에서 맡은 장점을 가지고 있으며 여러 응용분야에서 사용될 수 있기 때문에 다방면에서 시도되고 있다. 본 시스템은 모폴로지 연산과 클러스트링을 이용하여 자동차 번호판 전체 영역을 추출하는 방법을 사용한다. 다음으로 구번호판에서 신번호판으로 넘어가는 과도기적 단계에 있는 번호판들의 특징인 용도기능의 표시문자의 위치 차이를 이용하여 구 번호판과 신번호판을 먼저 분류한다. 분류된 번호판에서 두 번호판의 차이점인 차종기초 표시영역의 숫자를 나누어서 세그멘테이션함으로서 기존의 연구방법보다 개선된 세그멘테이션 능력과 이로 인하여 향상된 번호판 인식결과를 얻을 수 있다.

• 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%.