Chinese Font Generation from Stroke Semantic and Attention Mechanism

In order to help designers develop computer Chinese fonts efficiently,a Chinese character font generation algorithm is proposed.The algorithm can generate full font character images with a relatively consistent style based on small font datasets.For the sake of solving problems in stroke adhesion and structural errors when using adversarial generative network(GAN),the prior information of stroke semantics is introduced to improve the deep neural network for alleviating wrong stroke generation.Moreover,a skip connection module equipped with attention mechanism is integrated in the network model to reduce incomplete font structure and training period.In this module,features in encoder can be projected to that in decoder,which is helpful in eliminating the information loss of the decoder so as to avoid generating structural errors.Comparative experiments between the proposed algorithm and other methods including pix2pix,zi2zi and DCFont are conducted on public Chinese font datasets.The results show the proposed algorithm is better that the others from the perspective of SSIM and average in loss function.The industry application and scope of our algorithm are given in finally.