• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.528-531
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• 2003

Recognition of licence plate becomes a key issue to many traffic related application such as road traffic monitoring or parking lots access control. In this paper, the brightness, YIQ and HSI methods were used to locate a license. After the characters in license plate were extracted, template matching method was applied for character recognitions. To test the performance of the proposed algorithm, images of seventy vehicle were tested. The success rates for license plate and character recognition were approximately 99%, and 96%, respectively

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 추계학술발표대회
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• pp.333-334
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• 2009

In this paper, I propose the novel method based on Labeled Hough transform and GLCM(Grey-Level Co-occurrence Matrix) for license plate detection. A lot of conventional methods have been proposed to detect the license plate, but those are useless in order to detect the license plate well in case of dark or unstable images. Histogram equalization is preprocessed to each image before applying this method. As a result, the license plate is detected accurately

• 한국문헌정보학회지
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• 제44권2호
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• pp.263-283
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• 2010

자유이용라이선스는 저작물의 자유로운 이용과 창작 활성화를 위한 좋은 수단이며, 이는 저작물의 이용에 대하여 저작권 분쟁과 불법이용에 대한 예방 차원의 역할을 수행 할 수 있다. 본 연구는 자유이용라이선스 서비스 및 국내외 기술적 현황을 분석하고, 기술적 요소에 따른 요구사항을 반영하여, 라이선스 적용, 검색 및 활용을 위한 기술적, 서비스 및 연계 관점의 전략 방안을 제시한다. 그 결과 자유이용라이선스의 활성화를 위한 디지털 아카이빙과 의 연계 방안과 UCI 식별체계와의 연계 방안, 그리고 공공정보의 활용방안 등 3가지 활용방안을 제안한다.

• 정보처리학회논문지C
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• 제11C권6호
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• pp.739-746
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• 2004

라이선스 메커니즘은 DRM(Digital Rights Management) 시스템의 핵심 요소 중의 하나이다. 라이선스 메커니즘은 DRM 시스템의 자원(resources), 자원의 사용 주체(principals), 자원에 대한 사용 규칙(usage rules) 등을 명확하게 식별하고 강제하기 위한 핵심적인 기능을 담당하도록 설계된다. 하지만 기존의 라이선스 메커니즘들은 가정이나 회사의 어떤 부서와 같은 그룹 내의 멤버들이 어떤 컨텐츠론 공유하고자 할 때 공유에 관련된 융통성이 부족하다. 본 논문은 그룹에 등록된 여러 명의 멤버들이 DRM 기술로 보호된 디지털 컨텐츠를 안전하고 융통성 있게 공유할 수 있도록 하는 새로운 라이선스 메커니즘을 제안한다. 본 논문이 제안하는 멤버/그룹 라이선스 메커니즘은 그룹 라이선스, 멤버 라이선스, 허가들(grants)간의 파생 관계 등의 개념들을 도입하여 기존의 라이선스 메커니즘을 확장한다.

• 한국임상약학회지
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• 제23권1호
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• pp.71-76
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• 2013

Background (Purpose): The objective of this study was to investigate regulations on pharmacist and other healthcare professional license examination in Korea. Specific aim was focused on the implementation of new regulatory system governing pharmacist license examination particularly on naming of the subjects, method of the examination, and discrimination of pass or fail. Method: Laws and regulations of Korea on the examination system were retrieved from sources posted in Ministry of Government Legislation. Results: Two major regulatory differences were found between pharmacist and other healthcare professionals license examination systems. Firstly, the regulation on pharmacist license examination was ruled by the enforcement ordinance of parent law (Pharmacy Law) while it was ruled by enforcement regulation of parent law (Medical Law). Secondly, minimum grade requirement for pharmacist and other healthcare professionals was differently set up: 40% for each single subject in pharmacist and average of 40% for each group of several subjects in other healthcare professionals. Conclusion: Since pharmacist profession has drastically changed from drug-makers to drug-users during the recent decades, it is desirable to have the regulations on pharmacist license examination system amended in harmony with other laws and regulations of Korea and other major developed countries. Two-step examination for pharmacist license appears worthwhile to implement for balancing the two key functions of the pharmacist being drug-makers and drug-users.

• 한국산업융합학회 논문집
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• 제24권5호
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• pp.555-562
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• 2021

In this paper, we propose a sorting method for extracting accurate license plate information, which is currently used in Korea, after detecting objects using YOLO. We propose sorting methods for the five types of vehicle license plates managed by the Ministry of Land, Infrastructure and Transport by classifying the plates with the number of lines, Korean characters, and numbers. The results of experiments with 5 license plates show that the proposed algorithm identifies all license plate types and information by focusing on the object with high reliability score in the result label file presented by YOLO and deleting unnecessary object information. The proposed method will be applicable to all systems that recognize license plates.

• 한국IT서비스학회지
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• 제20권6호
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• pp.97-105
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• 2021

Recently, with the development of IT technology, unmanned systems are being introduced in many industrial fields, and one of the most important factors for introducing unmanned systems in the automobile field is vehicle licence plate recognition(VLPR). The existing VLPR algorithms are configured to use image processing for a specific type of license plate to divide individual areas of a character within the plate to recognize each character. However, as the number of Korean vehicle license plates increases, the law is amended, there are old-fashioned license plates, new license plates, and different types of plates are used for each type of vehicle. Therefore, it is necessary to update the VLPR system every time, which incurs costs. In this paper, we use an object detection algorithm to detect character regardless of the format of the vehicle license plate, and apply a handwritten Hangul recognition(HHR) algorithm to enhance the recognition accuracy of a single Hangul character, which is called a Hangul unit. Since Hangul unit is recognized by combining initial consonant, medial vowel and final consonant, so it is possible to use other Hangul units in addition to the 40 Hangul units used for the Korean vehicle license plate.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.121-126
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• 2022

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.

• 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.

• 정보처리학회논문지D
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• 제9D권6호
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• pp.1113-1118
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• 2002

자동차 번호판 인식 시스템에서 가장 중요한 요소는 자동차 이미지로부터 번호판 영역을 정확히 검출해 내는 것이다. 자동차 이미지에서 번호판 영역을 추출하기 위한 방법으로 색상과 밝기 정보와 자동차 번호판의 가로 : 세로 비율 등 번호판을 인식할 수 있는 정보를 혼용한 ACL 알고리즘을 제안한다 ACL 알고리즘을 사용함으로써 기존의 색상 정보나 명암 정보만을 이용할 경우 자동차 번호판 영역 추출이 잘되지 않는 문제를 해소시켜 준다. 본 논문에서 제안하는 ACL 알고리즘은 자동차 이미지에서 번호판 영역을 추출하기 위하여 색상 정보와 명암 정보, 기타 자동차 번호판을 판단할 수 있는 정보를 모두 이용한다. ACL 알고리즘을 이용하여 번호판 추출 실험을 한 결과 97%의 추출률을 보였다. ACL 알고리즘을 이용하여 추출된 번호판을 이용하여 문자 영역, 문자 인식을 실험한 결과 92%의 결과를 보였다.