• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.526-535
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• 2005

우리나라의 주민등록증은 주소지, 주민등록 변호, 얼굴사진, 지문 등 개개인의 방대한 정보를 가진다. 현재의 플라스틱 주민등록증은 위조 및 변조가 쉽고 날로 전문화 되어가고 있다. 따라서 육안으로 위조 및 변조 사실을 쉽게 확인하기가 어려워 사회적으로 많은 문제를 일으키고 있다. 이에 본 논문에서는 주민등록증 영상을 자동 인식할 수 있는 개선된 ART2 기반 RBF 네트워크와 얼굴인증을 이용한 주민등록증 자동 인식 방법을 제안한다. 제안된 방법은 주민등록증 영상에서 주민등록번호와 발행일을 추출하기 위하여 영상을 소벨마스크와 미디언 필터링을 적용한 후에 수평 스미어링을 적용하여 주민등록번호와 발행일 영역을 검출한다. 그리고 4 방향 윤곽선 추적 알고리즘으로 개별 문자를 추출하기 위한 전 단계로 주민등록증 영상에 대해 고주파 필터링을 적용하여 주민등록증 영상 전체를 이진화 한다. 이진화된 주민등록영상에서 COM 마스크를 적용하여 주민등록번호와 발행일 코드를 복원하고 검출된 각 영역에 대해 4 방향 윤곽선 추적 알고리즘으로 개별 문자를 추출한다. 추출된 개별 문자는 개선된 ART2 기반 RBF 네트워크를 제안하여 인식에 적용한다. 제안된 ART2 기반 RBF 네트워크는 학습 성능을 개선하기 위하여 중간충과 출력층의 학습에 퍼지 제어 기법을 적용하여 학습률을 동적으로 조정한다. 얼굴인증은 템플릿 매칭 알고리즘을 이용하여 얼굴 템플릿 데이터베이스를 구축하고 주민등록증애서 추출된 얼굴영역과의 유사도를 측정하여 주민등록증 얼굴 영역의 위조여부를 판별한다.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.591-600
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• 2006

This paper describes the development and implementation of a algorithm to decompose word images from image regions mixed text/graphics in document images using statistical analyses. To decompose word images from image regions, the character components need to be separated from graphic components. For this process, we propose a method to separate them with an analysis of box-plot using a statistics of structural components. An accuracy of this method is not sensitive to the changes of images because the criterion of separation is defined by the statistics of components. And then the character regions are determined by analyzing a local crowdedness of the separated character components. finally, we devide the character regions into text lines and word images using projection profile analysis, gap clustering, special symbol detection, etc. The proposed system could reduce the influence resulted from the changes of images because it uses the criterion based on the statistics of image regions. Also, we made an experiment with the proposed method in document image processing system for keyword spotting and showed the necessity of studying for the proposed method.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.29-35
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• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.1-9
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• 2014

Recently, a study is conducting to image collection and auto detection of attribute information using mobile mapping system. The road sign attribute information detection is difficult because of various size and placement, interference of other facilities like trees. In this study, a text detection method that does not rely on a Korean character template is required to successfully detect the target text when a variety of differently sized texts are present near the target texts. To overcome this, the method of incremental right-to-left blob projection (IRBP) was suggested as a solution; the potential and improvement of the method was also assessed. To assess the performance improvement of the IRBP that was developed, the IRBP method was compared to the existing method that uses Korean templates through the 60 videos of street signs that were used. It was verified that text detection can be improved with the IRBP method.

• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.35-43
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• 2004

In this paper, we proposed a vehicle recognition and electronic toll collection system with detection and classification of vehicle identification mark and emblem as well as recognition of vehicle license plate to unman toll fee collection system or incoming/outcoming vehicles to an institution. In the proposed algorithm, we first process pre-processing step such as noise reduction and thinning from the rear side input image of vehicle and detect vehicle mark, emblem and license plate region using intensity variation informations, template masking and labeling operation. And then, we classify the detected vehicle features regions into vehicle mark and emblem as well as recognize characters and numbers of vehicle license plate using hybrid and seven segment pattern vector. To show the efficiency of the proposed algorithm, we tested it on real vehicle images of implemented vehicle recognition system in highway toll gate and found that the proposed method shows good feature detection/classification performance regardless of irregular environment conditions as well as noise, size, and location of vehicles. And also, the proposed algorithm may be utilized for catching criminal vehicles, unmanned toll collection system, and unmanned checking incoming/outcoming vehicles to an institution.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.113-130
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• 2004

Most multimedia data contain text to emphasize the meaning of the data, to present additional explanations about the situation, or to translate different languages. But, the left makes it difficult to reuse the images, and distorts not only the original images but also their meanings. Accordingly, this paper proposes a support vector machines (SVMs) and spatiotemporal restoration-based approach for automatic text detection and removal in video sequences. Given two consecutive frames, first, text regions in the current frame are detected by an SVM-based texture classifier Second, two stages are performed for the restoration of the regions occluded by the detected text regions: temporal restoration in consecutive frames and spatial restoration in the current frame. Utilizing text motion and background difference, an input video sequence is classified and a different temporal restoration scheme is applied to the sequence. Such a combination of temporal restoration and spatial restoration shows great potential for automatic detection and removal of objects of interest in various kinds of video sequences, and is applicable to many applications such as translation of captions and replacement of indirect advertisements in videos.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.318-320
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• 1999

본 논문에서는 디지털 카메라를 통해 얻어진 자동차 영상으로부터 이산코사인변환(Discrete Cosin Transform : DCT)를 이용한 자동차번호판 추출방법을 제안한다. 번호판은 문자와 배경으로 이루어져 있으며 번호판 내에는 문자들이 조밀하게 모여 있다는 특징과 번호판 영역이 직사각형으로 되어 있다는 것을 이용하여 DCT에 의해서 자동차영상에서 수직, 수평, 대각선 성분만을 추출한후 이 추출된 에지영상에서 코릴레이션(Correlation)을 이용하여 번호판영역을 검출하고 이 검출된 번호판영역을 투영 히스토그램(Histogram)에 의해서 날씨가 흐리거나 아주 밝거나 밤에 찍은 영상들에 대해서는 번호판 추출이 힘들었다. 그러나 제안된 본 논문은 날씨와 납과 밤에 상관없이 일관된 번호판 영상을 추출할 수 있었다.

• Smart Media Journal
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• v.5 no.1
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• pp.15-23
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• 2016

This paper proposes an effective algorithm of Korean license plate recognition. By applying Haar-like feature and Canny edge detection on a captured 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 classified using neural networks trained by backpropagation algorithm to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

• Smart Media Journal
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• v.9 no.2
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• pp.92-98
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• 2020

This paper proposes a vehicle license plate recognition system using light weight deep learning models without high-end server. The proposed license plate recognition system consists of 3 steps: [license plate detection]-[character area segmentation]-[character recognition]. SSD-Mobilenet was used for license plate detection, ResNet with localization was used for character area segmentation, ResNet was used for character recognition. Experiemnts using Samsung Galaxy S7 and LG Q9, accuracy showed 85.3% accuracy and around 1.1 second running time.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.336-342
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• 2002

통신 수단의 발달로 인터넷을 이용한 E-mail이 활성화된 지금 명함에서 E-mail정보는 빠지지 않고 표기된다. 만약 수작업으로 관련 정보를 입력했던 것을 명함이미지에서 E-mail을 자동으로 추출한다면 유용할 것이다. 본 논문에서는 명함 영상에서 E-mail 영역을 검출하기 위한 텍스처 특성을 분석하여 텍스트 영역을 분할하고 연결화소를 이용한 개별문자 추출 방법을 통해 at symbol@을 인식하는 방법에 관하여 논한다.