• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.850-861
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• 2020

Hangul is a language that is composed of initial, medial, and final syllables. It has 11,172 characters. For this reason, the current method of designing all the characters by hand is very expensive and time-consuming. In order to solve the problem, this paper proposes an automatic Hangul font generation system and evaluates the standards for mapping Hangul characters to produce an effective automated Hangul font generation system. The system was implemented using character generation engine based on deep learning CycleGAN. In order to evaluate the criteria when mapping characters in pairs, each criterion was designed based on Hangul structure and character shape, and the quality of the generated characters was evaluated. As a result of the evaluation, the standards designed based on the Hangul structure did not affect the quality of the automated Hangul font generation system. On the other hand, when tried with similar characters, the standards made based on the shape of Hangul characters produced better quality characters than when tried with less similar characters. As a result, it is better to generate automated Hangul font by designing a learning method based on mapping characters in pairs that have similar character shapes.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.73-78
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• 2021

Recently, as interest in deep learning has increased, many researches in various fields using deep learning techniques have been conducted. Studies on automatic generation of fonts using deep learning-based generation models are limited to several languages such as Roman or Chinese characters. Generating Korean font is a very time-consuming and expensive task, and can be easily created using deep learning. For research on generating Korean fonts, it is important to prepare a Korean font dataset from the viewpoint of process automation in order to keep pace with deep learning-based generation models. In this paper, we propose a Korean font dataset for deep learning-based Korean font research and describe a method of constructing the dataset. Based on the Korean font data set proposed in this paper, we show the usefulness of the proposed dataset configuration through the process of applying it to a deep learning Korean font generation application.

• 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.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.699-705
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• 2015

Thousands of glyphs are necessary for Hangul font generation. It is mandatory to generate the required glyphs before producing Hangul font. This paper, entitled "Multiple Master Glyph Algorithm", presents an process that generates a target number of glyphs automatically from a very small number of glyphs by using a combination rule setting and a glyph interpolation method. A font editor, which is able to generate Hangul glyphs or fonts, is developed based on this algorithm. The editor generates a target number of fundamental glyphs automatically by using a combination rule setting and four master glyphs, which can be set up by a user. The automatically generated glyphs can be used to generate a target font by combining KSX1001 standard Hangul 2350 characters or Unicode standard Hangul 11172 characters automatically. The efficiency of the proposed Hangul editor is analyzed quantitatively in this paper through application to several commercial typefaces.

• 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 the Korean Institute of Telematics and Electronics A
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• v.29A no.2
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• pp.100-106
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• 1992

In this pape, we designed and implemented a hardware accelerator for the generation of bit map font from Hangul outline font description for LBP (Laser Beam Printer) and screen applications Whole system was implemented as a double size PC/AT application board which consists of processing bolck and display block. The processing block has a master processor (MC68000)and two slave processors which are MC56001 and KAFOG chip responsible for the short vector generation. In the display block, TMS34061 was used for monitor display and GP425 was used for LBP print out. The resolution of the monitor is 640$\times$480 and that of LBP is 2385$\times$3390. The current system called KHGB90-B generates about 100 characters per second where each character consists of 32$\times$32 bits

• 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.

• 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.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.5
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• pp.802-807
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• 1990

In this thesis, we represent the study on the design and implementation of the hangul graphic board which generate bit map font data from the boundary information of korean or chines fonts. The implemented graphic board consists of a TMS34010 Graphic System Processor (GSP) and two TMS320C25 Digital Signal Processor (DSP), and there is shared memory which consists of two memory blocks with same address for which is possible parallel processing between two processors. And in using DSP, we propose an efficient algorithm for calculation of Bezier curve which require much times to calculate bit map data font from the boundary information.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.12
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• pp.894-906
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• 2006

There are many difficulties to develop a mobile contents due to many constraints on mobile environments. It is difficult to make a good mobile contents with only visual reduction of existing contents on wire Internet. Therefore, it is essential to devise the data representation and to develop the authoring tool to meet the needs of the mobile contents market. We suggest the compact mobile contents to learn Chinese characters and developed its authoring tool. The animation which our system produces is realistic as if someone writes letters with pen or brush. Moreover, our authoring tool makes a user generate a Chinese character animation easily and rapidly although she or he has not many knowledge in computer graphics, mobile programming or Chinese characters. The method to generate the stroke animation is following: We take basic character shape information represented with several contours from TTF(TrueType Font) and get the information for the stroke segmentation and stroke ordering from simple user input. And then, we decompose whole character shape into some strokes by using polygonal approximation technique. Next, the stroke animation for each stroke is automatically generated by the scan line algorithm ordered by the stroke direction. Finally, the ordered scan lines are compressed into some integers by reducing coordinate redundancy As a result, the stroke animation of our system is even smaller than GIF animation. Our method can be extended to rendering and animation of Hangul or general 2D shape based on vector graphics. We have the plan to find the method to automate the stroke segmentation and ordering without user input.