• International Journal of Contents
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• 제14권4호
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• pp.86-91
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• 2018

Text has evolved along with the history of art as a means of communicating human intentions and emotions. In addition, text visualization artworks have been combined with the social form and contents of new media to produce social messages and related meanings. Recently, in text visualization artworks combined with digital media, communication forms with viewers are changing instantly and interactively, and viewers are actively participating in creating artworks by direct engagement. Interactive text visualization with additional viewer's interaction, generates external dynamics from text shapes and internal dynamics from embedded meanings of text. The purpose of this study is to propose a visual dynamics model to express the dynamics of text and to implement a text visualization system based on the model. It uses the deconstruction of the imaged text to create an interactive text visualization system that reacts to the gestures of the viewer in real time. Visual Transformation synchronized with the intentions of the viewer prevent the text from remaining in the interpretation of language symbols and extend the various meanings of the text. The visualized text in various forms shows visual dynamics that interpret the meaning according to the cultural background of the viewer.

• ETRI Journal
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• 제33권1호
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• pp.78-88
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• 2011

In this paper, we present the Touch Text exTractor (Touch TT), an interactive text segmentation tool for the extraction of scene text from camera-based images. Touch TT provides a natural interface for a user to simply indicate the location of text regions with a simple touchline. Touch TT then automatically estimates the text color and roughly locates the text regions. By inferring text characteristics from the estimated text color and text region, Touch TT can extract text components. Touch TT can also handle partially drawn lines which cover only a small section of text area. The proposed system achieves reasonable accuracy for text extraction from moderately difficult examples from the ICDAR 2003 database and our own database.

• Educational Technology International
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• 제13권1호
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• pp.49-77
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• 2012

The development of mobile devices and network technology is changing the ways in which people communicate with one another. Mobile text message has emerged as one of the most frequently used form of communication, which also gave rise to various non-verbal texts such as emoticons. Nonetheless, the use of text messages has largely been denied in education because text messages often involve colloquial and non-verbal texts considered inappropriate or grammatically incorrect by the teacher. In efforts to provide a theoretical framework to better understand mobile e-text communication, this research compared the practical usages of non-verbal texts in the mobile e-learning environment. The study developed three types of text messages according to the degree of using non-verbal texts and their phraseology as instructors' messages, which were then distributed to 259 students via mobile text messaging. The perceptions of students were analyzed using a semantic differential scale and a questionnaire. The results showed clear differences in students' perceptions of non-verbal text and traditional text, and that optimally designed non-verbal texts turned out to encourage the students' interaction the most out of the three types of text messages. Following the discussion of the results, an expanded theoretical framework beyond Ong's concepts of orality and literacy is also suggested to understand the evolution of mobile e-text communication in education.

• 한국인터넷방송통신학회논문지
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• 제13권5호
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• pp.37-47
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• 2013

텍스트 마이닝은 비정형화된 텍스트를 분석하여 그 안에 내재된 패턴, 추세, 분포 등의 고급정보들을 추출하는 분야이다. 텍스트 마이닝은 기본적으로 비정형 데이터를 가정하므로 텍스트를 단순화된 모델로 표현하는 것이 필요하다. 현재까지 가장 많이 사용되고 있는 모델은 텍스트를 단순한 단어들의 집합으로 표현한 벡터공간 모델이다. 그러나 최근 들어 단어들의 의미적 관계까지 표현하기 위해 그래프를 이용한 텍스트 표현 모델을 많이 사용하고 있다. 본 논문에서는 텍스트 마이닝을 위한 기존의 연구 중에서 그래프에 기반한 텍스트 표현 모델의 방법들과 그들의 특징들을 기술한다. 또한 그래프 기반 텍스트 마이닝의 향후 발전방향에 대해서도 논한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.771-789
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• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

• 스마트미디어저널
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• 제1권4호
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• pp.27-34
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• 2012

In this paper, a novel text binarization is presented that can deal with some complex conditions, such as shadows, non-uniform illumination due to highlight or object projection, and messy backgrounds. To locate the target text region, a focus line is assumed to pass through a text region. Next, connected component analysis and stroke width estimation based on location information of the focus line is used to locate the bounding box of the text region, and each box of connected components. A series of classifications are applied to identify whether each CC(Connected component) is text or non-text. Also, a modified K-means clustering method based on an HCL color space is applied to reduce the color dimension. A text binarization procedure based on location of text component and seed color pixel is then used to generate the final result.

• 대한인간공학회지
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• 제26권2호
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• pp.149-155
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• 2007

Recently, various devices requiring text input such as mobile phone IPTV, PDA and UMPC are emerging. The frequency of text entry for them is also increasing. This study was focused on the evaluation of Korean text entry interface. Various models to evaluate text entry interfaces have been proposed. Most of models were based on human cognitive process for text input. The cognitive process was divided into two components; visual scanning process and finger movement process. The time spent for visual scanning process was modeled as Hick-Hyman law, while the time for finger movement was determined as Fitts' law. There are three questions on the model-based evaluation of text entry interface. Firstly, are human cognitive processes (visual scanning and finger movement) during the entry of text sequentially occurring as the models. Secondly, is it possible to predict real text input time by previous models. Thirdly, does the human cognitive process for text input vary according to users' text entry speed. There was time gap between the real measured text input time and predicted time. The time gap was larger in the case of participants with high speed to enter text. The reason was found out investigating Eye-Hand Coordination during text input process. Differently from an assumption that visual scan on the keyboard is followed by a finger movement, the experienced group performed both visual scanning and finger movement simultaneously. Arrival Lead Time was investigated to measure the extent of time overlapping between two processes. 'Arrival Lead Time' is the interval between the eye fixation on the target button and the button click. In addition to the arrival lead time, it was revealed that the experienced group uses the less number of fixations during text entry than the novice group. This result will contribute to the improvement of evaluation model for text entry interface.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제16권1호
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• pp.110-114
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• 2010

본 논문에서는 TextRank 알고리즘을 이용한 문서 범주화 방법에 대해 기술한다. TextRank 알고리즘은 그래프 기반의 순위화 알고리즘이다. 문서에서 나타나는 각각의 단어를 노드로, 단어들 사이의 동시출현성을 이용하여 간선을 만들면 문서로부터 그래프를 생성할 수 있다. TextRank 알고리즘을 이용하여 생성된 그래프로부터 중요도가 높은 단어를 선택하고, 그 단어와 인접한 단어를 묶어 하나의 자질로 사용하여 문서 분류를 수행하였다. 동시출현 자질(인접한 단어 쌍)은 단어 하나가 갖는 의미를 보다 명확하게 만들어주므로 문서 분류에 좋은 자질로 사용될 수 있을 것이라 가정하였다. 문서 분류기로는 지지 벡터 기계, 베이지언 분류기, 최대 엔트로피 모델, k-NN 분류기 등을 사용하였다. 20 Newsgroups 문서 집합을 사용한 실험에서 모든 분류기에서 제안된 방법을 사용했을 때, 문서 분류 성능이 향상된 결과를 확인할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4072-4091
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• 2015

A separation of text and non-text elements plays an important role in document layout analysis. A number of approaches have been proposed but the quality of separation result is still limited due to the complex of the document layout. In this paper, we present an efficient method for the classification of text and non-text components in document image. It is the combination of whitespace analysis with multi-layer homogeneous regions which called recursive filter. Firstly, the input binary document is analyzed by connected components analysis and whitespace extraction. Secondly, a heuristic filter is applied to identify non-text components. After that, using statistical method, we implement the recursive filter on multi-layer homogeneous regions to identify all text and non-text elements of the binary image. Finally, all regions will be reshaped and remove noise to get the text document and non-text document. Experimental results on the ICDAR2009 page segmentation competition dataset and other datasets prove the effectiveness and superiority of proposed method.

• 한국지능시스템학회논문지
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• 제17권3호
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• pp.297-303
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• 2007

본 논문에서는 디지털 비디오를 위한 획기반 자막 추출 알고리즘을 제안한다. 제안된 알고리즘은 자막 탐지, 자막 위치 찾기 자막 분리 단계와 분리된 자막에 대한 기하학적 검증 과정으로 구성된다. 자막 탐지 단계는 연속적으로 입력되는 프레임 중 자막이 존재하는 프레임을 찾는 단계로써, 주어진 프레임으로부터 자막이 될 가능성이 높은 점, 즉 씨앗점을 추출한 다음 씨앗점에 대하여 모폴로지 연산을 수행한다. 자막 위치 찾기 단계는 자막이 존재하는 프레임에서 자막의 위치를 찾는 단계로써, 씨앗점을 포함하는 에지에 대한 모폴로지 연산과 프로젝션을 통해 수행된다. 자막 분리 단계에서는 자막과 배경의 색상 분포와 복잡한 배경을 고려하여 자막을 강건하게 분리한다. 마지막으로 자막에 대한 사전 정보를 이용하여 분리된 자막에 대한 기하학적 검증 과정을 수행하여 최종 결과를 얻는다.