基于结构约束生成对抗网络的书法汉字生成

Calligraphic Chinese Characters Generation Based on Generative Adversarial Networks with Structural Constraint

目前书法汉字的生成研究在汉字生成过程中需要大量先验汉字组成信息,不仅对前期数据收集工作的要求较高,而且影响研究成果的扩展性.针对此问题,文中提出基于结构约束的条件堆叠生成对抗网络的书法汉字生成方法.将源汉字图像直接提取的汉字笔迹作为结构约束条件,通过条件堆叠生成对抗网络模型生成高质量的书法汉字.同时提出通过伪目标样本的半监督学习方法,用于解决书法汉字数据集较少的问题,也可生成训练不可见的书法汉字.实验表明,在使用少样本的特定风格的书法汉字数据集的前提下,文中方法可生成更高质量的书法汉字.

A large amount of prior composition information of Chinese characters is required for the generation of calligraphic Chinese characters.Moreover,the previous data collection is demanding work,and the scalability of the research results is easily affected.To solve this problem,a method of Chinese calligraphy characters generation based on structure constraint using conditional stack generative adversarial networks is proposed.The Chinese character handwriting extracted directly from the source Chinese character image is considered as the structure constraint condition.High-quality calligraphic Chinese characters are generated by the condition stack generative adversarial network model.A semi-supervised learning method based on pseudo target samples is proposed for the dataset lacking of calligraphic Chinese characters.Furthermore,the unseen calligraphic Chinese characters during training are generated as well.Experiments show the proposed method can generate higher-quality calligraphy Chinese characters under the premise of using a few samples of a specific style of calligraphic Chinese character dataset.