• Journal of Information Processing Systems
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• v.2 no.3 s.4
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• pp.178-182
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• 2006

Children exhibit different behaviors, skills, and motivations. The main aim of this research was to investigate children's sensibility factors for icons, and to look for the best typeface for application to Web-Based Instruction (WBI) for e-Learning. Three types of icons were used to assess children's sensibilities toward metaphors: text-image, representational, and spatial mapping. Through the factor analysis, we found that children exhibited more diverse reactions to the text-image and representational types of icons than to the spatial mapping type of icons. Children commonly showedn higher sensibilities to the aesthetic-factor than to the familiarity-factor or the brevity-factor. In addition, we propose a collaborative-typeface system, which recommends the best typeface for children regarding the readability and aesthetic factor in WBI. Based on these results, we venture some suggestions on icon design and typeface selection for e-Learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2312-2325
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• 2013

In this paper, we propose a preprocessing method that it is to correct the distortion of text area in Korean signboard images as a preprocessing step to improve character recognition. Distorted perspective in recognizing of Korean signboard text may cause of the low recognition rate. The proposed method consists of four main steps and eight sub-steps: main step consists of potential vertical components detection, vertical components detection, text-boundary estimation and distortion correction. First, potential vertical line components detection consists of four steps, including edge detection for each connected component, pixel distance normalization in the edge, dominant-point detection in the edge and removal of horizontal components. Second, vertical line components detection is composed of removal of diagonal components and extraction of vertical line components. Third, the outline estimation step is composed of the left and right boundary line detection. Finally, distortion of the text image is corrected by bilinear transformation based on the estimated outline. We compared the changes in recognition rates of OCR before and after applying the proposed algorithm. The recognition rate of the distortion corrected signboard images is 29.63% and 21.9% higher at the character and the text unit than those of the original images.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.205-210
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• 2008

A new approach is proposed for restoration of corrupted regions and segmentation in natural text images. The challenge is to fill in the corrupted regions on the basis of color feature analysis by second order symmetric stick tensor. It is show how feature analysis can benefit from analyzing features using tensor voting with chromatic and achromatic components. The proposed method is applied to text images corrupted by manifold types of various noises. Firstly, we decompose an image into chromatic and achromatic components to analyze images. Secondly, selected feature vectors are analyzed by second-order symmetric stick tensor. And tensors are redefined by voting information with neighbor voters, while restore the corrupted regions. Lastly, mode estimation and segmentation are performed by adaptive mean shift and separated clustering method respectively. This approach is automatically done, thereby allowing to easily fill-in corrupted regions containing completely different structures and surrounding backgrounds. Applications of proposed method include the restoration of damaged text images; removal of superimposed noises or streaks. We so can see that proposed approach is efficient and robust in terms of restoring and segmenting text images corrupted.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.4
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• pp.153-166
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• 2008

According to the growth of the internet and the usage of software agents, the CAPTCHA that is a method for taking apart humans and computers has been widely deployed and used. As the results of many research activities, the CAPTCHA, which is spoken for a distorted image material including random text, has known to be easily breakable via artificial intelligence techniques. As one of alternatives for those text-based CAPTCHAs, methods using photos are concerned and various image-based CAPTCHAs are suggested. However, image-based CAPTCHAs still have some problems. In this paper, we discuss what are the problems in each image-based CAPTCHA and propose a new image-based CAPTCHA using image composition as the solution of those problems. Furthermore, for the secure operation of the CAPTCHA, we suggest a communication protocol that works without the virtual session and consider possible security and usability problems in the protocol.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.926-930
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• 2006

Current image retrieval techniques have shortcomings that make it difficult to search for images based on a semantic understanding of what the image is about. Since an image is normally associated with multiple contexts (e.g. when and where a picture was taken,) the knowledge of these contexts can enhance the quantity of semantic understanding of an image. In this paper, we present a context-aware image retrieval system, which uses the context information to infer a kind of metadata for the captured images as well as images in different collections and databases. Experimental results show that using these kinds of information can not only significantly increase the retrieval accuracy in conventional content-based image retrieval systems but decrease the problems arise by manual annotation in text-based image retrieval systems as well.

• The Journal of the Korea Contents Association
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• v.3 no.1
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• pp.14-20
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• 2003

In this paper efficient skew estimation and correction approaches are proposed. To detect the skew of text images, Hough transform using the perpendicular angle view property and shape moments are peformed. The resultant primary text skew angle is used to align the original text. The performance evaluations of the proposed methods with respect to running time are shown.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.9 no.1
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• pp.39-52
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• 1998

The purpose of this study is to design the solution for creating full-text database of Korean dissertaion, After considering file formats for text based and image based database, Viewer, Search, Copy Right, Abstracts and Indexes, situation of Korea University Library, decided the principles of creating database. And suggested the design to produce the database for Korea University Library, that is easy to get file format conversion in case of the introducing new technology for the future.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.295-302
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• 2010

Example-based super resolution(EBSR) is a method to reconstruct high-resolution images by learning patch-wise correspondence between high-resolution and low-resolution images. It can reconstruct a high-resolution from just a single low-resolution image. However, when it is applied to a text image whose font type and size are different from those of training images, it often produces lots of noise. The primary reason is that, in the patch matching step of the reconstruction process, input patches can be inappropriately matched to the high-resolution patches in the patch dictionary. In this paper, we propose a new patch matching method to overcome this problem. Using an image observation model, it preserves the correlation between the input and the output images. Therefore, it effectively suppresses spurious noise caused by inappropriately matched patches. This does not only improve the quality of the output image but also allows the system to use a huge dictionary containing a variety of font types and sizes, which significantly improves the adaptability to variation in font type and size. In experiments, the proposed method outperformed conventional methods in reconstruction of multi-font and multi-size images. Moreover, it improved recognition performance from 88.58% to 93.54%, which confirms the practical effect of the proposed method on recognition performance.

• Asia Marketing Journal
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• v.22 no.3
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• pp.71-85
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• 2020

Social media has emerged as a major hub of engagement between brands and consumers in recent years, and allows user-generated content to serve as a powerful means of encouraging communication between the sides. However, it is challenging to negotiate user-generated content owing to its lack of structure and the enormous amount generated. This study focuses on the hashtag, a metadata tag that reflects customers' brand perception through social media platforms. Online users share their knowledge and impressions using a wide variety of hashtags. We examine hashtags that co-occur with particular branded hashtags on the social media platform, Instagram, to derive insights about brand perception. We apply text mining technology and network analysis to identify the perceptions of brand images among consumers on the site, where this helps distinguish among the diverse personalities of the brands. This study contributes to highlighting the value of hashtags in constructing brand personality in the context of online marketing.

• International Journal of Contents
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• v.11 no.2
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• pp.15-19
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• 2015

In this paper, we propose an efficient document layout analysis algorithm that includes table detection. Typical methods of document layout analysis use the height and gap between words or columns. To correspond to the various styles and sizes of documents, we propose an algorithm that uses the mean value of the distance transform representing thickness and compare with components in the local area. With this algorithm, we combine a table detection algorithm using the same feature as that of the text classifier. Table candidates, separators, and big components are isolated from the image using Connected Component Analysis (CCA) and distance transform. The key idea of text classification is that the characteristics of the text parallel components that have a similar thickness and height. In order to estimate local similarity, we detect a text region using an adaptive searching window size. An improved adaptive run-length smoothing algorithm (ARLSA) was proposed to create the proper boundary of a text zone and non-text zone. Results from experiments on the ICDAR2009 page segmentation competition test set and our dataset demonstrate the superiority of our dataset through f-measure comparison with other algorithms.