Browse > Article
http://dx.doi.org/10.3745/KTSDE.2021.10.2.73

Hangul Font Dataset for Korean Font Research Based on Deep Learning  

Ko, Debbie Honghee (숭실대학교 컴퓨터공학과)
Lee, Hyunsoo (숭실대학교 IT유통물류학과)
Suk, Jungjae (숭실대학교 (주)팅크웨어)
Hassan, Ammar Ul (숭실대학교 컴퓨터공학과)
Choi, Jaeyoung (숭실대학교 컴퓨터학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.2, 2021 , pp. 73-78 More about this Journal
Abstract
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.
Keywords
Deep Learning; Font Data; Automatic Font Generation; Hangul Font Dataset; Hangul Font;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Krizhevsky, V. Nair, and G. Hinton, "The CIFAR-10 dataset," [Internet] https://www.cs.toronto.edu/-kriz/ cifar.html.
2 S. Azadi, M. Fisher, V. Kim, Z. Wang, E. Shechtman, and T. Darrell, "Multi-Content GAN for Few-Shot Font Style Transfer," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.7564-7573, 2018.
3 Y. Gao, Y. Guo, Z. Lian, Y. Tang, and J. Xiao, "Artistic glyph image synthesis via one-stage few-shot learning," ACM Transactions on Graphics, Vol.38, No.6, 2019.
4 FontForge, Spline font Database [Internet], https://fontforge.org/docs/techref/sfdformat.html.
5 The Unicode Consortium, The Unicode Standard [Internet], https://www.unicode.org/standard/standard.html.
6 H. Min, A. Hassan, J. Suk. and J. Choi, "Font image dataset auto generating module based on unicode (in Korean)," in Proc. Korea Computer Congress 2020 (KCC2020), Vol.46, No.01, pp.1818-1820, 2019.
7 P. Isola, J.-Y. Zhu, T. Zhou, and A. Efros, "Image-to-image translation with conditional adversarial networks," in Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2017.
8 D. H. Ko, A. Hassan, J. Suk, and J. Choi, "Korean font synthesis with GANs," in International Journal of Computer Theory and Engineering, Vol.12, No.4, pp.92-96, 2020.   DOI
9 Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.8, pp.1798-1828, 2013.   DOI
10 Y. Jiang, Z. Lian, Y. Tang, and J. Xiao, "DCFont: An end-to-end deep chinese font generation system," SIGGRAPH Asia 2017, Technical Briefs, 2017.
11 Y. Tian, "zi2zi: Master chinese calligraphy with conditional adversarial networks," [Internet] https://github.com/kaonashi-tyc/zi2zi.
12 J. Choi and S. Hong, "Aspects of the development of Korean font design in the digital era," Journal of Digital Design, Vol.8, No.2, pp.173-182, Apr. 2008.   DOI
13 Korean Publishing Research Institute, Basic research in Hangul Style (in Korean), Korean Publishing Research Institute, 1990.
14 Y. LeCun, C. Cortes, and C. Burges, "The mnist database of handwritten digit," [Internet] http://yann.lecun.com/exdb/mnist/.