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http://dx.doi.org/10.9717/kmms.2020.23.8.953

Automatic Extraction of Hangul Stroke Element Using Faster R-CNN for Font Similarity  

Jeon, Ja-Yeon (Dept. of IT Engineering, Graduate School, Sookmyung Women's University)
Park, Dong-Yeon (Dept. of IT Engineering, Sookmyung Women's University)
Lim, Seo-Young (Dept. of IT Engineering, Sookmyung Women's University)
Ji, Yeong-Seo (Dept. of IT Engineering, Sookmyung Women's University)
Lim, Soon-Bum (Dept. of IT Engineering and Research Institute of ICT Convergence, Sookmyung Women's University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.8, 2020 , pp. 953-964 More about this Journal
Abstract
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.
Keywords
Characteristics of Hangul Shape; Object Detection of Stroke Element; Automatic Extraction of Object Deletion; Hangul Font Similarity;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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