JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
SkelGAN: A Font Image Skeletonization Method |
| 영문제목(English Title) |
SkelGAN: A Font Image Skeletonization Method |
| 저자(Author) |
Debbie Honghee Ko
Ammar Ul Hassan
Saima Majeed
Jaeyoung Cho
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| 원문수록처(Citation) |
VOL 17 NO. 01 PP. 0001 ~ 0013 (2021. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this research, we study the problem of font image skeletonization using an end-to-end deep adversarial network, in contrast with the state-of-the-art methods that use mathematical algorithms. Several studies have been concerned with skeletonization, but a few have utilized deep learning. Further, no study has considered generative models based on deep neural networks for font character skeletonization, which are more delicate than natural objects. In this work, we take a step closer to producing realistic synthesized skeletons of font characters. We consider using an end-to-end deep adversarial network, SkelGAN, for font-image skeletonization, in contrast with the state-of-the-art methods that use mathematical algorithms. The proposed skeleton generator is proved superior to all well-known mathematical skeletonization methods in terms of character structure, including delicate strokes, serifs, and even special styles. Experimental results also demonstrate the dominance of our method against the state-of-the-art supervised image-to-image translation method in font character skeletonization task.
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| 키워드(Keyword) |
Generative Adversarial Network
Image-to-Image Translation
Skeletonization
Style Transfer
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| 파일첨부 |
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