| Abstrakt: |
This paper introduces a deep learning-based oracle style migration and generation technique, whose core capability lies in converting arbitrary modern Chinese characters into characters with oracle features. This paper proposes a StyleGAN-based oracle style migration and generation model, which utilizes a GAN framework of style encoder, image reconstructor, and multidiscriminator, thus realizing the generation of high-quality, high-resolution, diverse, and realistic oracle images. This paper also provides an in-depth evaluation of this paper's model in terms of multiple dimensions and key metrics, including style migration effect, style generation effect, generation efficiency and expert evaluation, and compares it with other comparative models to demonstrate the superiority and innovation of this paper's model. This paper provides an effective and innovative solution to solve the problem of recognizing and understanding ancient texts. [ABSTRACT FROM AUTHOR] |
