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%.

翻译：古代文字研究对考古学和文字学具有重要价值。摄影文字是重要的实物资料，但人工识别摄影文字极其耗时且依赖专业知识，因此自动分类具有迫切需求。然而，由于标注数据匮乏，现有分类性能有限。数据生成是缓解数据稀缺的低成本有效方案，但摄影古代文字字形多样、背景纹理复杂，使得生成任务困难重重，现有方法效果不佳。本文提出无监督生成对抗网络AGTGAN，通过显式的全局与局部字形风格建模、笔画感知纹理迁移以及关联对抗学习机制，生成具有多样字形与逼真纹理的文字。在OBC306和CSDD等摄影古代文字数据集上评估，本方法在多项指标上超越现有最优方法，生成样本的多样性与真实性显著更优。借助生成图像，在最大规模摄影甲骨文字数据集上的实验表明，本方法可将分类准确率提升最高16.34%。