SkelGAN: A Font Image Skeletonization Method

초록

In this research, we study the problem of font image skeletonization using an end-to-end deep adversarialnetwork, in contrast with the state-of-the-art methods that use mathematical algorithms. Several studies havebeen concerned with skeletonization, but a few have utilized deep learning. Further, no study has consideredgenerative models based on deep neural networks for font character skeletonization, which are more delicatethan natural objects. In this work, we take a step closer to producing realistic synthesized skeletons of fontcharacters. 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 generatoris 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 dominanceof our method against the state-of-the-art supervised image-to-image translation method in font characterskeletonization task.

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