• Journal of Digital Contents Society
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• v.17 no.5
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• pp.349-357
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• 2016

The purpose of this study is to analyze demands of education training about copyright for Infant and early childhood education institutions. This study investigated demands of education training about copyright for infant and early childhood education institutions targeting 248 directors and teachers. Using a questionnaire, research data was analyzed using the program spss. Demands of education training about copyright for infant and early childhood education institutions appeared differently depending on education institution type, position, education career, etc. copyright training topics include the highest demands for understanding of the copyright associated with the work of the early childhood education. Training contents had higher demands for using typeface(font) and video material. So these results reported that directors and teachers require copyright education relating to the work of Early childhood education. The results of this study suggest the implications for developing and implementing copyright training programs for Infant and Early childhood education institutions.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.255-256
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• 2019

한글 폰트를 만드는 데는 자음+모음 조합으로 약 11,500자 정도의 글자가 필요하다. 디자이너가 글자 하나씩 전부 디자인 하는 것도 굉장한 부담요소이고, 한글폰트를 제작하는데 있어 3개월 이상의 소요 기간과 3000만 원 이상의 비용부담 또한 무시 못 할 요소이다. 게다가 카피라이트 폰트에 대한 저작권 문제 또한 골칫거리다. 그래서 이를 최소한으로 하고자 딥 러닝의 방식중 하나인 GAN(생성적 적대 신경망)을 통해서 디자이너가 399자만 작성하고 나머지는 컴퓨터가 디자이너의 폰트 디자인을 인식하고 자동으로 만들어 주는 프로그램을 고안하였다.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.419-420
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• 2018

글자체(폰트)는 저작권으로 보호받지 못하지만 글자(폰트)프로그램은 저작권법 컴퓨터 프로그램 보호 규정에 따라 저작물로 보호된다. 저작권법이 글자체 그 자체는 보호하지 않지만, 컴퓨터프로그램으로 보호하는 양극간의 괴리 때문에 폰트 제작 회사가 무리한 법적용을 폰트 사용 편집회사들에게 주장하는 경우가 자주 발생하고 있다. 본고에서는 폰트 프로그램의 저작권 적용 범위를 확인하고 위반이 되는 경우와 그렇지 않은 경우를 구별한 뒤, 폰트 제작 회사의 실제 사례를 예로 들어 문제점을 밝혀보기로 한다.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.953-964
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• 2020

Ever since media contents took over the world, the importance of typography has increased, and the influence of fonts has be n recognized. Nevertheless, the current Hangul font system is very poor and is provided passively, so it is practically impossible to understand and utilize all the shape characteristics of more than six thousand Hangul fonts. In this paper, the characteristics of Hangul font shapes were selected based on the Hangul structure of similar fonts. The stroke element detection training was performed by fine tuning Faster R-CNN Inception v2, one of the deep learning object detection models. We also propose a system that automatically extracts the stroke element characteristics from characters by introducing an automatic extraction algorithm. In comparison to the previous research which showed poor accuracy while using SVM(Support Vector Machine) and Sliding Window Algorithm, the proposed system in this paper has shown the result of 10 % accuracy to properly detect and extract stroke elements from various fonts. In conclusion, if the stroke element characteristics based on the Hangul structural information extracted through the system are used for similar classification, problems such as copyright will be solved in an era when typography's competitiveness becomes stronger, and an automated process will be provided to users for more convenience.