Browse > Article
http://dx.doi.org/10.5351/KJAS.2019.32.4.573

A study in Hangul font characteristics using convolutional neural networks  

Hwang, In-Kyeong (Begas Inc.)
Won, Joong-Ho (Department of Statistics, Seoul National University)
Publication Information
The Korean Journal of Applied Statistics / v.32, no.4, 2019 , pp. 573-591 More about this Journal
Abstract
Classification criteria for Korean alphabet (Hangul) fonts are undeveloped in comparison to numerical classification systems for Roman alphabet fonts. This study finds important features that distinguish typeface styles in order to help develop numerical criteria for Hangul font classification. We find features that determine the characteristics of the two different styles using a convolutional neural network to create a model that analyzes the learned filters as well as distinguishes between serif and sans-serif styles.
Keywords
convolutional neural networks; local feature; fontstyle; hangul; visualization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Bauermeister, B. (1988). A Manual of Comparative Typography: The PANOSE System, Van Nostrand Reinhold, New York.
2 Dieleman, S., Schluter, J, Raffel, C., et al. (2015). Lasagne: First Release, (Version v0.1), Zenodo.
3 Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
4 Lim, S. B. and Kim, H. Y. (2016). Hangul font classification study based on numerical analysis, Extended Abstracts of HCI Korea, 180-181.
5 Simonyan, K., Vedaldi, A., and Zisserman, A. (2013). Deep inside convolutional networks: visualising image classification models and saliency maps, CoRR, abs/1312.6034.
6 Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. A. (2014). Striving for simplicity: the all convolutional net, CoRR, abs/1412.6806.
7 Zeiler, M. D. and Fergus, R. (2014). Visualizing and understanding convolutional networks, ECCV, 818-833.
8 LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning, Nature, 521, 436.   DOI