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딥러닝 학습을 이용한 한글 글꼴 자동 제작 시스템에서 글자 쌍의 매핑 기준 평가Evaluation of Criteria for Mapping Characters Using an Automated Hangul Font Generation System based on Deep Learni

Other Titles
Evaluation of Criteria for Mapping Characters Using an Automated Hangul Font Generation System based on Deep Learni
Authors
전자연지영서박동연임순범
Issue Date
Jul-2020
Publisher
한국멀티미디어학회
Keywords
Automated Hangul Font Generation System; Font Generation Using CycleGAN; Evaluation of Mapping Characters; Completeness of Generated Characters; Similarity of Characters Style
Citation
멀티미디어학회논문지, v.23, no.7, pp.850 - 861
Journal Title
멀티미디어학회논문지
Volume
23
Number
7
Start Page
850
End Page
861
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/1347
DOI
10.9717/kmms.2020.23.7.850
ISSN
1229-7771
Abstract
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
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ICT융합공학부 (IT공학전공)
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