Enhancing Chinese Calligraphy Generation with Contour and Region-aware Attention

Chinese calligraphy generation is an important problem involved in many applications. Existing methods generally regard it as an image-to-image translation problem. However, existing models meet the challenge of poor generation performance for the Chinese calligraphy generation due to the lack of effective guided information. In this paper, we propose a novel model called CRA-GAN for the Chinese calligraphy generation through utilizing the contours of calligraphy characters as certain guided information and introducing a region-aware attention module to capture their content regions, motivated by the observation that the contour and content region provide certain delicate characteristics on the calligraphy style and content, respectively. Noticing that there is usually a large glyph difference between the source and target fonts, resulting in the degradation of model performance, we borrow the idea of adaptive pre-deformation from the literature to address this issue. A series of experiments are conducted to show the effectiveness of the suggested contour and region-aware attention, as well as the used adaptive pre-deformation operation. The outperformance of the proposed model over the state-of-the-art models is also demonstrated through extensive quantitative and qualitative comparisons over nine Chinese calligraphic font datasets.