Chinese calligraphy is the writing of Chinese characters as an art form performed with brushes so Chinese characters are rich of shapes and details. Recent studies show that Chinese characters can be generated through image-to-image translation for multiple styles using a single model. We propose a novel method of this approach by incorporating Chinese characters' component information into its model. We also propose an improved network to convert characters to their embedding space. Experiments show that the proposed method generates high-quality Chinese calligraphy characters over state-of-the-art methods measured through numerical evaluations and human subject studies.
CalliMaster: Mastering Page-level Chinese Calligraphy via Layout-guided Spatial Planning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Faculty of Engineering and IT, University of Technology Sydney（悉尼大学工程与信息学院）；Xiamen University（厦门大学）；The Hong Kong University of Science and Technology（香港科技大学）
PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy
Nanyang Technological University（南洋理工大学）；Seafog AI（海雾人工智能）；San Diego State University（圣地亚哥州立大学）；Heriot-Watt University（赫瑞瓦特大学）；Kyoto University（京都大学）；Rhode Island School of Design（罗德岛设计学院）；Ocean University of China（中国海洋大学）
Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters
Institute of Information Engineering, Chinese Academy of Sciences（信息工程研究所，中国科学院）；Tencent（腾讯）；Anyang Normal University（安阳师范学院）；The Palace Museum（故宫博物院）；Nankai University（南开大学）
Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
