Computer vision-driven automated generation and style simulation of calligraphic fonts

As computer vision technology grows quickly, the automatic creation and stylistic replication of calligraphic fonts has become a major area of study in digital art and cultural heritage. This paper suggests a multi-task learning model that combines a convolutional neural network (CNN) and a generative adversarial network (GAN). The conditional generative adversarial network (cGAN) is used to do the style migration and to make high-quality calligraphic fonts automatically and to fine-tune the simulation of different styles. The method works better than several popular generation methods when it comes to image structure fidelity, style expressiveness, and perceptual consistency, showing that it has both artistic and practical significance. This paper's research gives new ideas and technical help for using computer vision to digitise traditional art.