  The study of ancient writings has great value for archaeology and philology.
Essential forms of material are photographic characters, but manual
photographic character recognition is extremely time-consuming and
expertise-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 of
photographic ancient characters make the generation task difficult, leading to
the unsatisfactory results of existing methods. In this paper, we propose an
unsupervised generative adversarial network called AGTGAN. By the explicit
global and local glyph shape style modeling followed by the stroke-aware
texture transfer, as well as an associate adversarial learning mechanism, our
method can generate characters with diverse glyphs and realistic textures. We
evaluate our approach on the photographic ancient character datasets, e.g.,
OBC306 and CSDD. Our method outperforms the state-of-the-art approaches in
various metrics and performs much better in terms of the diversity and
authenticity of generated samples. With our generated images, experiments on
the largest photographic oracle bone character dataset show that our method can
achieve a significant increase in classification accuracy, up to 16.34%.
