• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.319-321
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• 2001

본 논문에서는 스캐너로 입력된 한글 문서 영상에서 한글 문자를 인식하는 방법을 제시한다. 입력된 한글 문자를 한글의 구조적 특징에 따라 6개의 유형으로 분리하고, 각 유형에서의 모음의 형태학적 특징에 근거하여 모음을 인식한다. 각 유형에서의 자음의 인식을 위해서 가중치 패턴 클러스터를 생성하고 생성된 클러스터와 원영상간의 유사도 측정을 통해 자음을 인식하게 된다. 오인식 가능성이 있는 자음은 오인식 교정을 위한 세부 유사도 매칭과정을 통해 최종적으로 인식된다. 제안하는 알고리즘을 바탕으로 실험한 결과 스캐너로 입력받은 상용 한글 문자 14,983자에 대해 최종 95.68%의 인식률을 보였으며, 차후 정형화된 한글 문서 인식 시스템에 응용될 수 있을 것이다.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.267-286
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• 2023

The conversational agents such as AI speakers utilize voice conversation for human-computer interaction. Voice recognition errors often occur in conversational situations. Recognition errors in user utterance records can be categorized into two types. The first type is misrecognition errors, where the agent fails to recognize the user's speech entirely. The second type is misinterpretation errors, where the user's speech is recognized and services are provided, but the interpretation differs from the user's intention. Among these, misinterpretation errors require separate error detection as they are recorded as successful service interactions. In this study, various text separation methods were applied to detect misinterpretation. For each of these text separation methods, the similarity of consecutive speech pairs using word embedding and document embedding techniques, which convert words and documents into vectors. This approach goes beyond simple word-based similarity calculation to explore a new method for detecting misinterpretation errors. The research method involved utilizing real user utterance records to train and develop a detection model by applying patterns of misinterpretation error causes. The results revealed that the most significant analysis result was obtained through initial consonant extraction for detecting misinterpretation errors caused by the use of unregistered neologisms. Through comparison with other separation methods, different error types could be observed. This study has two main implications. First, for misinterpretation errors that are difficult to detect due to lack of recognition, the study proposed diverse text separation methods and found a novel method that improved performance remarkably. Second, if this is applied to conversational agents or voice recognition services requiring neologism detection, patterns of errors occurring from the voice recognition stage can be specified. The study proposed and verified that even if not categorized as errors, services can be provided according to user-desired results.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.383-385
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• 2000

차량 번호판 문자영상으로부터 여러 가지 특징 추출 방법을 조합하여 입력특징소를 재구성하고, 신경망을 이용하여 문자를 인식한다. 속도 개선을 위해 특별한 전처리 과정없이 이치화와 크기 정규화만을 수행한 후 그물망 방법과 BLT 방법, 정규화된 투영값 특정 방법을 조합하여 입력특징소를 구성한다. 본 연구에서는 숫자 인식에서 그물망 방법과 BLT 방법을 이용하여 잡음으로 인한 유사 문자의 오인식을 해결하였고, 문자 인식에서는 정규화된 투영값 특징을 이용하여 문자의 유형을 분류한 후 자소를 개별적으로 인식하였다. 이로써 모음 인식 경우에 중요한 역할을 하는 작은 획의 영역에 BLT 방법을 사용함으로 기존 연구에서의 모음 오인식 문제를 해결하였다.

• The KIPS Transactions:PartB
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• v.8B no.3
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• pp.223-230
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• 2001

본 논문에서는 접촉된 숫자들에 대한 효과적인 분할 기반 인식 방법을 제안하였다. 접촉 숫자들을 연결획 정보와 분할 후보점을 기반으로 여섯 개의 접촉 유형으로 구분하였다. 전체 후보 분할점을 해석하여 네 개의 최종 후보 분할점을 도출하므로써 과 분할로 인한 오인식을 줄일 수 있도록 하였다. 이 방법에서는 다수의 분할 후보점으로부터 신뢰성이 높은 소규모의 분할 후보점들에 대해 우선권을 부여하는 방식으로 최종 분할 후보점들을 찾고 인식을 시도하기 때문에 전통적으로 분할기반 방식의 인식에서 초래되는 오분할에 의한 치명적인 오인식률을 줄일 수 있도록 하였다. NIST 접촉숫자 데이터 베이스에 대한 실험 결과 92.5%의 비교적 높은 인식 성능을 얻을 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.810-812
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• 2015

문자인식 즉 OCR(Optical Character Recognition)기술은 광학적으로 인식할 수 있는 문자를 컴퓨터가 읽을 수 있도록 하는 기술을 뜻한다. 문자인식의 근간이 되는 방법은 스트링 매칭 기법이 사용되어 왔지만 한글의 경우 자음, 모음, 자음 조합으로 만 가지 유형이 넘고, 더욱이 상용한자와 영어를 섞어 쓰기 때문에 오인식되는 경우가 많다. 본 논문에서는 한글이 수직선, 수평선, 사선과 같이 방향성이 강한 선소들로 구성되어 있다는 점을 이용하여 한글의 인식률을 높이는 방법을 제안하였다.

• MALSORI
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• no.46
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• pp.77-86
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• 2003

Channel distortion and coarticulation effect in the Korean connected digit telephone speech make it difficult to achieve high performance of connected digit recognition in the telephone environment. In this paper, as a basic research to improve the recognition performance of Korean connected digit telephone speech, recognition error patterns are investigated and analyzed. Korean connected digit telephone speech database released by SiTEC and HTK system are used for recognition experiments. Both DWFBA and MRTCN methods are used for feature extraction and channel compensation, respectively. Experimental results are discussed with our findings.

• Proceedings of the KSPS conference
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• 2003.05a
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• pp.115-118
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• 2003

Channel distortion and coarticulation effect in the connected digit telephone speech make it difficult to recognize, and degrade recognition performance in the telephone environment. In this paper, as a basic research to improve the recognition performance of Korean connected digit telephone, error patterns are investigated and analyzed. Telephone digit speech database released by SITEC with HTK system is used for recognition experiments. Both DWFBA and MRTCN methods are used for feature extraction and channel compensation, respectively. Experimental results are discussed with our findings.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.117-126
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• 2022

Currently, the number of bridge structures in Korea is continuously increasing and enlarged, and the number of old bridges that have been in service for more than 30 years is also steadily increasing. Bridge aging is being treated as a serious social problem not only in Korea but also around the world, and the existing manpower-centered inspection method is revealing its limitations. Recently, various bridge damage detection studies using deep learning-based image processing algorithms have been conducted, but due to the limitations of the bridge damage data set, most of the bridge damage detection studies are mainly limited to one type of crack, which is also based on a close set classification model. As a detection method, when applied to an actual bridge image, a serious misrecognition problem may occur due to input images of an unknown class such as a background or other objects. In this study, five types of bridge damage including crack were defined and a data set was built, trained as a deep learning model, and an open set recognition-based bridge multiple damage recognition model applied with OpenMax algorithm was constructed. And after performing classification and recognition performance evaluation on the open set including untrained images, the results were analyzed.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.97-111
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• 2009

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a signal intersection and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, we intend to present a technology capable of overcoming problems in which advanced existing technologies exhibited limitations in handling real-time due to large data capacity such as object separation of vehicles and tracking, which pose difficulties due to environmental diversities and changes at a signal intersection with complex traffic situations, as pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian complex model analytical method which has been considered the best among well-known environmental obstacle reduction methods. To prove that the technology developed by this research has performance advantage over existing automatic traffic accident recording systems, a test was performed by entering image data from an actually operating crossroad online in real-time. The test results were compared with the performance of other existing technologies.

• Journal of Science Education
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• v.44 no.3
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• pp.289-299
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• 2020

In this study, the definition, the level of difficulty, and the certainty of the understanding of 113 astronomy terminologies from 2009 revised middle school geoscience textbooks were examined. And through further interviews, the types of students' misconceptions about astronomy terminologies and their representative terms - examples of misconceptions were analyzed. The definitions of the terms presented by the students were largely classified as correct, low-level, and incorrect understanding. And low-level understanding was subdivided into high-level definition descriptions, undifferentiated concepts, and incorrect answers were subdivided into interference by scientific misconception and lack of prior knowledge. Given that the misconceptions due to terminologies can be distinguished from the prior misconception, the misconceptions due to terminologies can be effectively prevented by changing the term itself. In addition, students were aware of the advantages and disadvantages of metaphorical terms, and the recognition of their level of understanding is expected to be a good starting point considering that recognizing their own misconceptions is the first step in correcting them. Terminologies in science education is always an important subject of discussions, striving to select the right term according to the times, and scientific terms may change. It is expected that the results of this study will be the basis for discussions on the modification of terms.