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The study of ancient writings has great value for archaeology and philology.Essential forms of material are photographic characters, but manualphotographic character recognition is extremely time-consuming andexpertise-dependent. Automatic classification is therefore greatly desired.However, the current performance is limited due to the lack of annotated data.Data generation is an inexpensive but useful solution for data scarcity.Nevertheless, the diverse glyph shapes and complex background textures ofphotographic ancient characters make the generation task difficult, leading tothe unsatisfactory results of existing methods. In this paper, we propose anunsupervised generative adversarial network called AGTGAN. By the explicitglobal and local glyph shape style modeling followed by the stroke-awaretexture transfer, as well as an associate adversarial learning mechanism, ourmethod can generate characters with diverse glyphs and realistic textures. Weevaluate our approach on the photographic ancient character datasets, e.g.,OBC306 and CSDD. Our method outperforms the state-of-the-art approaches invarious metrics and performs much better in terms of the diversity andauthenticity of generated samples. With our generated images, experiments onthe largest photographic oracle bone character dataset show that our method canachieve a significant increase in classification accuracy, up to 16.34%.

AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation