Research on Handwriting Text Generation Algorithm Based on Generative Adversarial Network

Abstract

The main task of this paper is to study the handwriting text generation method based on deep learning. Through understanding the development status of the research, It can be found that the current research on the generation of different handwriting styles still has some obvious defects, such as the need for manual intervention in character segmentation, failure to capture the global handwriting style, style collapse, failure to generate arbitrary length characters, and text. Finally, this paper proposes a handwritten text generation algorithm combining advantages of convolutional network and Transformer. Specifically, this paper first constructs a lightweight backbone network and uses lightweight MobileNetv3 network as the backbone network to realize feature extraction of input images. The Efficient Channel Attention module is introduced to replace the SE attention module of MobileNetv3, which makes the network pay more attention to the global and local style features of handwritten images. In the feature extraction part, the network reduces the number of parameters, calculation amount and video memory occupation. It can also extract rich feature information. The FID and KID indexes of this algorithm obtained 20.28 and 9.07×10^-3 respectively, and the generation effect of handwritten pictures was excellent, which could effectively imitate the writing style of writers.