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http://dx.doi.org/10.9717/kmms.2020.23.11.1361

Analysis of Extraction Performance according to the Expanding of Applied Character in Hangul Stroke Element Extraction  

Jeon, Ja-Yeon (Dept. of IT Engineering, Graduate School, Sookmyung Women's University)
Lim, Soon-Bum (Dept. of IT Engineering and Research Institute ICT Convergence, Sookmyung Women's University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.11, 2020 , pp. 1361-1371 More about this Journal
Abstract
Fonts have developed as a visual element, and their influence has rapidly increased around the world. Research on font automation is actively being conducted mainly in English because Hangul is a combination character and the structure is complicated. In the previous study to solve this problem, the stroke element of the character was automatically extracted by applying the object detection by component. However, the previous research was only for similarity, so it was tested on various print style fonts, but it has not been tested on other characters. In order to extract the stroke elements of all characters and fonts, we performed a performance analysis experiment according to the expansion character in the Hangul stroke element extraction training. The results were all high overall. In particular, in the font expansion type, the extraction success rate was high regardless of having done the training or not. In the character expansion type, the extraction success rate of trained characters was slightly higher than that of untrained characters. In conclusion, for the perfect Hangul stroke element extraction model, we will introduce Semi-Supervised Learning to increase the number of data and strengthen it.
Keywords
Hangul Stroke Element; Expansion of Object Detection; Automatic Extraction; Components;
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Times Cited By KSCI : 7  (Citation Analysis)
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