Jangkyoung Park, Ammar Ul Hassan, Jaeyoung Choi

KIPS Transactions on Software and Data Engineering, Vol. 10, No. 11, pp. 473-482, Nov. 2021
https://doi.org/10.3745/KTSDE.2021.10.11.473,   PDF Download:
Keywords: Hangul Font Generation, Font Components, GAN, Few-shot
Abstract

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.

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