• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1835-1838
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• 2003

Texts in natural images contain significant and detailed informations about the images. Thus, to extract those texts correctly, we suggest a text extraction method using color variance feature. Generally, the texts in images have color variations with the backgrounds. Thus, if we express those variations in 3 dimensional RGB color space, we can emphasize the text regions that can be hard to be captured with a method using intensity variations in the gray-level images. We can even make robust extraction results with the images contaminated by light variations. The color variations are measured by color variance in this paper. First, horizontal and vertical variance images are obtained independently, and we can fine that the text regions have high values of the variances in both directions. Then, the two images are logically ANDed to remove the non-text components with only one directional high variance. We have applied the proposed method to the multiple kinds of the natural images, and we confirmed that the proposed feature can help to find the text regions that can he missed with the following features - intensity variations in the gray-level images and/or color continuity in the color images.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.44-51
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• 2009

Text understand in natural images has become an active research field in the past few decades. In this paper, we present an automatic recognition system in Korean signboards with a complex background. The proposed algorithm includes detection, binarization and extraction of text for the recognition of shop names. First, we utilize an elaborate detection algorithm to detect possible text region based on edge histogram of vertical and horizontal direction. And detected text region is segmented by clustering method. Second, the text is divided into individual characters based on connected components whose center of mass lie below the center line, which are recognized by using a minimum distance classifier. A shape-based statistical feature is adopted, which is adequate for Korean character recognition. The system has been implemented in a mobile phone and is demonstrated to show acceptable performance.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.251-256
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• 2022

범주 불균형은 분류 모델이 다수 범주에 편향되게 학습되어 소수 범주에 대한 분류 성능을 떨어뜨리는 문제를 야기한다. 언더 샘플링 기법은 다수 범주 데이터의 수를 줄여 소수 범주와 균형을 이루게하는 대표적인 불균형 해결 방법으로, 텍스트 도메인에서의 기존 언더 샘플링 연구에서는 단어 임베딩과 랜덤 샘플링과 같은 비교적 간단한 기법만이 적용되었다. 본 논문에서는 트랜스포머 기반 문장 임베딩과 군집화 기반 샘플링 방법을 통해 텍스트 데이터의 정보 손실을 최소화하는 언더샘플링 방법을 제안한다. 제안 방법의 검증을 위해, 감성 분석 실험에서 제안 방법과 랜덤 샘플링으로 추출한 훈련 세트로 모델을 학습하고 성능을 비교 평가하였다. 제안 방법을 활용한 모델이 랜덤 샘플링을 활용한 모델에 비해 적게는 0.2%, 많게는 2.0% 높은 분류 정확도를 보였고, 이를 통해 제안하는 군집화 기반 언더 샘플링 기법의 효과를 확인하였다.

• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.440-447
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• 2013

Recently corpus linguistic analyses enable researchers to examine meanings and structural features of data, that is not detected intuitively. While the potential of corpus linguistic techniques has been established and demonstrated for non-literary data, corpus stylistic analyses have been rarely performed in terms of the analysis of literature. Specifically this paper explores keywords and their role in text analysis, which is primary part of corpus linguistic analyses. This paper focuses on the application of techniques from corpus linguistics and the interpretation of results. This paper addresses the question of what is to be gained from keyword analysis by scrutinizing keywords in Shakespeare's Romeo and Juliet.

• Informatization Policy
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• v.25 no.4
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• pp.84-107
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• 2018

The existing research on domestic e-government trends in Korea has weaknesses in that it depends only on qualitative research methods. Therefore, a quantitative analysis was conducted through this study as of September 2018 based on the data from 1996 to 2017. A total of seven research topics were derived from text mining, of which the network centrality of the framework and public policy effect were identified as highly significant. The results of this study provide academic and policy implications for the development of e-government. including that using a quantitative analysis method instead of a qualitative method contributes to ensuring relative objectivity and diversity of learning.

• Electronics and Telecommunications Trends
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• v.4 no.1
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• pp.3-15
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• 1989

컴퓨터에 의한 처리 결과를 음성으로 회답해주는 각종 음성응답 기술의 현황과 응용 및 발전 방향을 정리하였고 이방면 신기술인 텍스트에 의한 음성합성에 관하여도 기술하였다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.267-275
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• 2000

최근 전자상거래가 활성화되면서, 거래 인증서와 같은 온라인 증명서가 광범위하게 사용되고 있다. 그리고 증명서의 위/변조기술이 발전함에 따라서 온라인 거래에 사용되는 증명서의 인증과 위조/변조를 방지하는 기술이 필요하게 되었다. 본 연구는 증명서의 인증에 필요한 기술로서, 메시지 인증함수가 가지는 성질을 포함하고 있다. 본 연구에서 개발한 알고리즘은 증명서에 포함된 텍스트문서가 위조/변조되었을 경우 그 변동 상황을 알아내며, 부정적으로 위조/변조된 부분을 검출하며, 변동상황 검출과 함께 원 증명서의 문서를 복원할 수 있는 기술이다. 만일 이 증명서에 대하여 변동이 진행된 흔적이 발견될 경우, 증명서를 인증하지 않으며, 삽입한 텍스트 데이터를 추출하고 변동을 확인하는 것과 함께 필요한 정보를 복원한다. 본 논문의 시험결과에 근거하면 256$\times$256BMP file Format 이미지에 3만2천자 정도의 텍스트문서를 삽입할 수 있었다.

• Annual Conference of KIPS
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• 2013.11a
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• pp.1121-1124
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• 2013

프로그램 실행 시 동화(텍스트)를 읽을 수 있게 하는 입력 파일을 받을 수 있게 만든다. 이렇게 받은 텍스트를 프로그램이 읽어 들여 알고리즘으로 대입한다. 알고리즘은 읽은 텍스트 중에 중요 단어를 뽑아내어 DB에 이에 알맞은 삽화를 요청하게 된다. 요청을 받은 데이터베이스는 다시 프로그램에 이미지를 보내주고, 프로그램은 이 삽화를 그 텍스트에 출력시키게 된다. 이 과정이 한 페이지를 만드는 과정이며, 원하지 않는 페이지는 건너 뛸 수 있게 한다. 이러한 작업을 반복하여 새로운 책 한권이 완성이 된다.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.97-110
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• 2018

With the advent of the 4-th industrial revolution, manufacturing companies have increasing interests in the realization of smart manufacturing by utilizing their accumulated facilities data. However, most previous research dealt with the structured data such as sensor signals, and only a little focused on the unstructured data such as text, which actually comprises a large portion of the accumulated data. Therefore, we propose an association rule mining based facility error pattern extraction framework, where text data written by operators are analyzed. Specifically, phrases were extracted and utilized as a unit for text data analysis since a word, which normally used as a unit for text data analysis, is unable to deliver the technical meanings of facility errors. Performances of the proposed framework were evaluated by addressing a real-world case, and it is expected that the productivity of manufacturing companies will be enhanced by adopting the proposed framework.

• Database Research
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• v.34 no.3
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• pp.3-13
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• 2018

Text classification is one of the text mining technologies that classifies a given textual document into its appropriate categories and is used in various fields such as spam email detection, news classification, question answering, emotional analysis, and chat bot. In general, the text classification system utilizes machine learning algorithms, and among a number of algorithms, naïve Bayes and support vector machine, which are suitable for text data, are known to have reasonable performance. Recently, with the development of deep learning technology, several researches on applying deep neural networks such as recurrent neural networks (RNN) and convolutional neural networks (CNN) have been introduced to improve the performance of text classification system. However, the current text classification techniques have not yet reached the perfect level of text classification. This paper focuses on the fact that the text data is expressed as a vector only with the word dimensions, which impairs the semantic information inherent in the text, and proposes a neural network architecture based upon the semantic tensor space model.