AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation

• URL: http://arxiv.org/abs/2303.07012v1
• Date: Mon, 13 Mar 2023 11:18:41 GMT
• Title: AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation
• Authors: Hongxiang Huang, Daihui Yang, Gang Dai, Zhen Han, Yuyi Wang, Kin-Man Lam, Fan Yang, Shuangping Huang, Yongge Liu, Mengchao He
• Abstract summary: We propose an unsupervised generative adversarial network called AGTGAN. By explicit global and local glyph shape style modeling, our method can generate characters with diverse glyphs and realistic textures. 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%.
• Score: 27.77329906930072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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