Research on the Recognition and Adaptive Generation Model of Traditional Painting and Calligraphy Style Based on Deep Learning

This study proposes a model for the recognition and adaptive generation of traditional painting and calligraphy styles based on deep learning, aiming to realize the automatic recognition and adaptive generation of the style of traditional painting and calligraphy works. First, the deep convolutional neural network (CNN) and residual network (ResNet) are used for style recognition, which significantly improves the accuracy of style classification, especially in complex calligraphy and painting styles. Second, the conditional generative adversarial network (cGAN) is used to realize the adaptive generation of traditional painting and calligraphy styles. By introducing style labels to effectively control the generation process, the generated images are superior to the traditional generative adversarial network (GAN) in terms of style consistency and detail performance. Through experimental evaluation, the style recognition model performs well in both accuracy and recall, while the generation model has made significant progress in the quality and structural similarity of the generated images. The research results show that the style recognition and generation method based on deep learning can provide strong support for the digital inheritance and innovative creation of traditional painting and calligraphy.