• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1166-1186
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• 2022

Artistic font design has become an integral part of visual media. However, without prior knowledge of the font domain, it is difficult to create distinct font styles. When the number of characters is limited, this task becomes easier (e.g., only Latin characters). However, designing CJK (Chinese, Japanese, and Korean) characters presents a challenge due to the large number of character sets and complexity of the glyph components in these languages. Numerous studies have been conducted on automating the font design process using generative adversarial networks (GANs). Existing methods rely heavily on reference fonts and perform font style conversions between different fonts. Additionally, rather than capturing style information for a target font via multiple style images, most methods do so via a single font image. In this paper, we propose a network architecture for generating multilingual font sets that makes use of geometric structures as content. Additionally, to acquire sufficient style information, we employ multiple style images belonging to a single font style simultaneously to extract global font style-specific information. By utilizing the geometric structural information of content and a few stylized images, our model can generate an entire font set while maintaining the style. Extensive experiments were conducted to demonstrate the proposed model's superiority over several baseline methods. Additionally, we conducted ablation studies to validate our proposed network architecture.

• KIISE Transactions on Computing Practices
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• v.22 no.9
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• pp.449-454
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• 2016

Radicals of Korean characters consist of some strokes, and complete letters are comprised of a combination of radicals. During the process of combining strokes and radicals, it requires a lot of effort as the size and the position of the components can be changed. Unlike this, METAFONT can improve the efficiency in the production process of fonts by changing its parameters. It also provides a system which can easily transform size and position of the strokes and radicals. We propose a structural Korean font generator which allows users to modify parameters of letters through METAFONT and generates a variety of fonts automatically. The suggested Korean font generator can be applied to font embedding and font editor.

• Journal of the Korea Computer Graphics Society
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• v.17 no.4
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• pp.11-21
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• 2011

This paper proposes a method for decomposing a Hangul glyph of outline fonts into its initial, medial and final components using statistical-structural information. In a font family, the positions of components are statistically consistent and the stroke relationships of a Hangul character reflect its structure. First, we create the component histograms that accumulate the shapes and positions of the same components. Second, we make pixel clusters from character image based on pixel direction probabilities and extract the candidate strokes using position, direction, size of clusters and adjacencies between clusters. Finally, we find the best structural match between candidate strokes and predefined character model by relaxation labeling. The proposed method in this paper can be used for a study on formative characteristics of Hangul font, and for a font classification/retrieval system.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.473-482
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• 2021

Although several Hangul generation models using deep learning have been introduced, they require a lot of data, have a complex structure, requires considerable time and resources, and often fail in style conversion. This paper proposes a model CKFont using the components of the initial, middle, and final components of Hangul as a way to compensate for these problems. The CKFont model is an end-to-end Hangul generation model based on GAN, and it can generate all Hangul in various styles with 28 characters and components of first, middle, and final components of Hangul characters. By acquiring local style information from components, the information is more accurate than global information acquisition, and the result of style conversion improves as it can reduce information loss. This is a model that uses the minimum number of characters among known models, and it is an efficient model that reduces style conversion failures, has a concise structure, and saves time and resources. The concept using components can be used for various image transformations and compositing as well as transformations of other languages.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1828-1841
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• 2017

In the previous research, we proposed a Kinetic typography font engine that can easily add motion to text with function call only. However, since it is aimed at constructing movements for a sentence, there is still inconvenience in the production of various kinetic typography motions in word or letter unit. We propose Kinetic Typical Extended Motion API(Application Programming Interface) that extends Kinetic Motion API. The extended Kinetic Typographic Font Engine aims to simplify the process of making kinetic typography in words and letters, including the kinetic typographic motion library provided as a function. In addition, various applications that can apply Kinetic typography A kinetic typography authoring interface is provided for facilitating the construction of a motion library for the robot.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.4
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• pp.311-325
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• 2009

This paper presents a method for analyzing Hangul glyphs with their outline fonts and obtaining intermediate fonts with two different fonts. The glyphs are represented and analyzed hierarchically such as characters, components(letters) and strokes. With the analysis results, we obtain several intermediate glyphs by morphing two different glyphs of same character. For a natural glyph contour morphing, we employ the curve morphing algorithm by weighted mean of strings. In addition, we provide four operations for transformation of glyphs with different topology. As a result, it is illustrated that the proposed Hangul glyphs morphing scheme is useful for new font generation from any exist fonts or handwritings.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.499-508
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• 2022

A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1361-1371
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• 2020

Fonts have developed as a visual element, and their influence has rapidly increased around the world. Research on font automation is actively being conducted mainly in English because Hangul is a combination character and the structure is complicated. In the previous study to solve this problem, the stroke element of the character was automatically extracted by applying the object detection by component. However, the previous research was only for similarity, so it was tested on various print style fonts, but it has not been tested on other characters. In order to extract the stroke elements of all characters and fonts, we performed a performance analysis experiment according to the expansion character in the Hangul stroke element extraction training. The results were all high overall. In particular, in the font expansion type, the extraction success rate was high regardless of having done the training or not. In the character expansion type, the extraction success rate of trained characters was slightly higher than that of untrained characters. In conclusion, for the perfect Hangul stroke element extraction model, we will introduce Semi-Supervised Learning to increase the number of data and strengthen it.

• Annual Conference of KIPS
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• 2017.04a
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• pp.9-12
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• 2017

FreeType is a rasterizer which is commonly used in different operating systems like Linux, android etc. Although FreeType is open source but it is not easy to add/remove modules and services etc. for developers. This paper proposes a new module for FreeType named as FreeType Outlet adapter (FOA). It enables to add/remove modules, services, functionality etc. inside FreeType. It acts as the bridge to add functions from outside FreeType to the inner core of FreeType. New font formats like METAFONT, animated fonts and customized fonts which currently are not supported by FreeType can be added with this FOA module.