@article{MBD6BDE8D,
title = "CKFont2: An Improved Few-Shot Hangul Font Generation Model 
Based on Hangul Composability",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2022",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2022.11.12.499",
author = "Jangkyoung Park/Ammar Ul Hassan/Jaeyoung Choi",
keywords = "Deep Learning, Few-Shot, Initial/Middle/Final Components, CKFont, Korean 14 Characters",
abstract = "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."
}