• 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.

• Smart Media Journal
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• v.10 no.1
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• pp.79-87
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• 2021

In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.

• 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.

• 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.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.793-798
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• 2000

As we review the mechanism regarding digital font generation and birth of TrueType font, we realizes that the process is composed of sequential steps such as contour fonts from glyph table. This fact implies that we propose classification of TrueType font in terms of segment width and the number of occurrence from the glyph data.

• 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

• 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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.9
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• pp.712-718
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• 1991

In this paper, we propose a graphic system for high speed generation of bitmap font data from the outline font data such as PostScript, etc. In desk-top publishing system. A VLSI chip called KAFOG was designed for the high-speed calculation of a cubic Bezier curve, which was implemented in 1.5\ulcorner CMOS gate array using 17,000 gates. A cubic Bezier curve is approximated by a set of line segments in KAFOG at the throughput of 250K curves per second with the clock frequency of 40 MHz. A prototype graphic system was developed using two MC6800 microprocessors and the KAFOG chip. Two microprocessors cooperate in a master and slave mode, and handshaking is used for communication between two processors. KAFOG chip, being controlled by the slave processor, operates as a coprocessor for the calculation of the outline font. The throughput of the prototype graphic system is 40 64$\times$64 outline fonts per sencond.

• Journal of Industrial Technology
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• v.11
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• pp.55-63
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• 1991

This paper presents a design of the accelerate circuit for the conversion of the vector font data into the bit-mapped image. Among the Bezier curve algorithm, the subdivision algorithm gives the good performance and easy hardware implementation. The sequencer is realized by the proprammable gate array and the processing unit is composed of EPLDs and TTL ICs.

• 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.