• Proceedings of the Korea Multimedia Society Conference
• /
• 1998.04a
• /
• pp.143-147
• /
• 1998

카메라 영상에 의한 자동차 번호판 인식시스템은 영상 획득, 번호판 추출, 전처리, 문자 분리, 문자 인식 등 크게 5자기의 핵심 부분으로 구성된다. 따라서 자동차 번호판 인식시스템의 성능을 향상시키기 위해서는 이들 부분들 각각의 성능의 최적화가 필요하다. 본 연구는 자동차 번호판 인식시스템의 여러 단계 중 전처리에 해당하는 번호판 영역의 이진화에 관한 연구로서, 기존의 단일 임계치 방법과 다중 임계치 방법이 해결하지 못했던 부분을 보완하는 새로운 다중 임계치 방법을 제안한다. 본 논문에서 제안하는 다중 임계치 알고리즘(Adaptive Multi-threshold Algorithm)을 사용함으로써 gray-level 번호판 영상에 대해서 보다 깨끗한 이진 영상을 얻을 수 있었으며, 또한 이 알고리즘은 번호판 영역의 밝기값이 고르지 않은 영상에 대해서도 효율적인 알고리즘 임을 알 수 있었다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37A no.9
• /
• pp.780-785
• /
• 2012

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plates center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate covered by the learning pattern, the effect of suppression learning of the number and headlight sections, as well as the effect of learning pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an underground parking garage demonstrated detection rates of 98.5%.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.04a
• /
• pp.775-778
• /
• 2002

본 논문에서는 도로를 주행하는 차량영상으로부터 번호판의 인식에 대한 연구이다. 차량을 검출하기 위해 두 프레임의 차를 이용하여 도로상에서 차량을 분리하였고, 번호판 영역을 추출하기 위해 명암도 변화의 파형 곡선 결과에 임계값을 적용하여 번호판을 추출하였다. 번호판 영역 검출은 96.05%의 검출결과를 얻었으며, 차량의 번호판 문자인식은 신경망을 통하여 학습 시켰 그 성능은 잭나이프 기법을 통해 측정하였다. 학습데이터에 대해서는 99.85 비학습데이터에 대해서는 88.15%의 인식율을 보였다.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.6
• /
• pp.76-86
• /
• 1999

In this paper, it is shown that the car number plate are segmented and extracted more efficiently by using wavelet transform. A car image is decomposed by wavelet transform, and the high frequency image of the decomposed image are selected as feature images. Three selected feature images are synthesized of a single feature image, and a region including the plate is segmented by the correlation coefficient between the feature image and the synthesized image. For segmented plate region, the car plate region is extracted by deciding the Y-axis region composed by vertical region, the car plate region is extracted by deciding the Y-axis region composed by vertical histogram and the X-axis region composed by the variance histogram. Some experiment results of the various image and shown. It has been shown from the results with the high rate of 96% that the car number plates can be segmented and extracted more extractly and efficiently than converntional method.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.5
• /
• pp.313-319
• /
• 2007

In this paper, we propose a novel method to extract an area of car licence plate and codes of vehicle number from a photographed car image using features on vertical edges and a new Fuzzy neural network algorithm to recognize extracted codes. Prewitt mask is used in searching for vertical edges for detection of an area of vehicle number plate and feature information of vehicle number palate is used to eliminate image noises and extract the plate area and individual codes of vehicle number. Finally, for recognition of extracted codes, we use the proposed Fuzzy neural network algorithm, in which FCM is used as the learning structure between input and middle layers and Max_Min neural network is used as the learning structure within inhibition and output layers. Through a variety of experiments using real 150 images of vehicle, we showed that the proposed method is more efficient than others.

• Journal of Korea Society of Industrial Information Systems
• /
• v.14 no.3
• /
• pp.1-14
• /
• 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 Korean Institute of Intelligent Systems
• /
• v.21 no.6
• /
• pp.705-711
• /
• 2011

This paper proposes an effective algorithm of license plate recognition for industrial applications. By applying Canny edge detection on a vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are compared with the pre-learned weighting values by backpropagation neural network to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.5
• /
• pp.73-81
• /
• 2007

This paper presents how to recognize the new-type vehicle license plate using multi-link recognizer after extract the features from characters. In order to assist this task, this paper proposed FE-MCBP to recognize each character that got through image preprocess, extract range of vehicle license plate and extract process of each character. FE-MCBP is the recognizer based on the features of the character, The recognizer is employed to identify the new-type vehicle licence plates which have both the hangul and the arabic numeral characters. And its recognition rate is improved 9.7 percent than the back propagation recognizer before. Also it makes use of extract of linear component and region coordinate generation technology to normalize a image of the tilted vehicle license plate. The recognition system of the new-type vehicle license plate make possible recognize a image of the tilted vehicle license plate when using this system. Also, this system can recognize the tilted or imperfect vehicle licence plates.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.26 no.12A
• /
• pp.2019-2026
• /
• 2001

In this paper, we proposed the car license plate extraction and recognition algorithm using both the dynamical thresholding method and the kohonen algorithm. In general, the areas of car license plate in the car images have distinguishing characteristics, such as the differences in intensity between the areas of characters and the background of the plates, the fixed ratio of width to height of the plates, and the higher dynamical thresholded density rate 7han the other areas, etc. Taking advantage of the characteristics, the thresholded images were created from the original images, and also the density rates were computed. A candidate area was selected, whose density rate was corresponding to the properties of the car license plate obtained from the car license plate. The contour tracking method by utilizing the Kohonen algorithm was applied to extract the specific area which included characters and numbers from an extracted plate area. The characters and numbers of the license place were recognized by using Kohonen algorithm. Kohonen algorithm was very effective o? suppressing noises scattered around the contour. In this study, 80 car images were tested. The result indicate that we proposed is superior in performance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.11
• /
• pp.2432-2438
• /
• 1997

This paper proposes recognition system using multi-backpropagation neural networks rather than single backpropagation neural network to enhance the rate of character recognition resultsing from extracting the region of velhicle number in that the image of vehicle number plate from CCD camera has a distinguish feature, that is, illumination of a pattern. The experiment in this paper shows an output that the method using multi-backpropagation neural networks rather than signal backpropagation neural network takes less training time for computation and also has higher recognition rage of vehicle number.