• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.337-340
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• 2000

Using the Information Retrieval systems on the Internet, the demand of exact and specific information has also been popularized. To offer exact information, there k3 been generalized demand of searching from the keyword of the shortened text and also of the full-text. This study is to suggest a scheme for full-text searches. It is to compare the existing scheme of information search and full-text information search with interMedia text. We suggest search methods for the full-text.

• Fashion & Textile Research Journal
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• v.11 no.6
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• pp.934-941
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• 2009

The purpose of this study is to examine by which correlation the pluralistic phenomenon in text is functioned in comparison with hair style and fashion in collection. As a result, the pluralistic image in text, which was shown in modern fashion, was indicated to be pluralistic phenomenon by gender, T.P.O, coordination, and material. The pluralistic image in text for hair style can be known to have been indicated to be the pluralistic phenomenon in text for gender and to be the pluralistic phenomenon in text according to material and cultural category. As for a method of this study, it did put limitation on the part that is shown in the fashion collection from 2001 to 2007, analyzed hair-style features centering on photos, which were extracted from style.com, the online site of specializing in fashion, and carried out a literature research side by side with the theoretical background on intertextuality. The analysis in work according to the pluralistic phenomenon in text made it possible for looking at with a new sight differently from the recognition in the past, and opened the potentiality for being able to understand lots of strange representations, which have been impossible so far. The process of imitating and reconstructing each text according to compositional principle led to possibly knowing the necessity of an artist's ability that can implement the originative world.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.742-751
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• 2005

This paper proposes a novel text image watermarking algorithm based on word classification. The words are classified into K classes using simple features. Several adjacent words are grouped into a segment. and the segments are also classified using the word class information. The same amount of information is inserted into each of the segment classes. The signal is encoded by modifying some inter-word spaces statistics of segment classes. Subjective comparisons with conventional word-shift algorithms are presented under several criteria.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.34-43
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• 2012

Determining the similarity between two strings can be applied various area such as information retrieval, spell checker and spam filtering. Similarity calculation between Korean strings based on dynamic programming methods firstly requires a definition of the similarity between phonemes. However, existing methods have a limitation that they use manually set similarity scores. In this paper, we propose a method to automatically calculate inter-phoneme similarity from a given set of variant words using a PAM-like probabilistic model. Our proposed method first finds the pairs of similar words from a given word set, and derives derivation rules from text alignment results among the similar word pairs. Then, similarity scores are calculated from the frequencies of variations between different phonemes. As an experimental result, we show an improvement of 10.1%~14.1% and 8.1%~11.8% in terms of sensitivity compared with the simple match-mismatch scoring scheme and the manually set inter-phoneme similarity scheme, respectively, with a specificity of 77.2%~80.4%.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.233-237
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• 2006

This study proposes an innovative measure for evaluating the performance of text clustering. In using K-means algorithm and Kohonen Networks for text clustering, the number clusters is fixed initially by configuring it as their parameter, while in using single pass algorithm for text clustering, the number of clusters is not predictable. Using labeled documents, the result of text clustering using K-means algorithm or Kohonen Network is able to be evaluated by setting the number of clusters as the number of the given target categories, mapping each cluster to a target category, and using the evaluation measures of text. But in using single pass algorithm, if the number of clusters is different from the number of target categories, such measures are useless for evaluating the result of text clustering. This study proposes an evaluation measure of text clustering based on intra-cluster similarity and inter-cluster similarity, what is called CI (Clustering Index) in this article.

• Proceedings of the Zoological Society Korea Conference
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• 1997.04a
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• pp.45-45
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• 1997

No Abstract, See Full Text

• Proceedings of the Zoological Society Korea Conference
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• 2003.08a
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• pp.35-35
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• 2003

No Abstract, See Full Text

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.582-589
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• 2014

Mobile phone users have been using the SMS to send and receive text messages between them. Recently, GSMA(GSM Association) proposed the next generation instant messaging service, RCS-e(Rich Communication Service-enhanced). This was unavoidable because new OTT(Over The Top) services have emerged that threat the SMS after smart phones became popular. However, the RCS-e has limitations that it cannot inter-operate with the SMS which is still widely used. This paper propose a software framework that inter-work SMS with RCS-e. Call flows for canceling wireless contracts and sending SMS messages to RCS-e users were defined. Also, methods for protocol conversion were defined to inter-work two services. The performance evaluation showed that the proposed framework does not increase loads on IMS(IP Multimedia Subsystem) infrastructure.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.427-438
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• 2023

Plenty of works have indicated that single image super-resolution (SISR) models relying on synthetic datasets are difficult to be applied to real scene text image super-resolution (STISR) for its more complex degradation. The up-to-date dataset for realistic STISR is called TextZoom, while the current methods trained on this dataset have not considered the effect of multi-scale features of text images. In this paper, a multi-scale and attention fusion model for realistic STISR is proposed. The multi-scale learning mechanism is introduced to acquire sophisticated feature representations of text images; The spatial and channel attentions are introduced to capture the local information and inter-channel interaction information of text images; At last, this paper designs a multi-scale residual attention module by skillfully fusing multi-scale learning and attention mechanisms. The experiments on TextZoom demonstrate that the model proposed increases scene text recognition's (ASTER) average recognition accuracy by 1.2% compared to text super-resolution network.

• Journal of KIISE:Software and Applications
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• v.27 no.6
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• pp.660-667
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• 2000

In this paper, a word segmentation method for handwritten Korean text line images is proposed. The method uses gap information to segment words in line images, where the gap is defined as a white run obtained after vertical projection of line images. Each gap is assigned to one of inter-word gap and inter-character gap based on gap distance. We take up three distance measures which have been proposed for the word segmentation of handwritten English text line images. Then we test three clustering techniques to detect the best combination of gap metrics and classification techniques for Korean text line images. The experiment has been done with 305 text line images extracted manually from live mail pieces. The experimental result demonstrates the superiority of BB(Bounding Box) distance measure and sequential clustering approach, in which the cumulative word segmentation accuracy up to the third hypothesis is 88.52%. Given a line image, the processing time is about 0.05 second.