Digital Protection and Inheritance of Intangible Cultural Heritage using Progressive Conditional Generative Adversarial Network with Vision Transformer

Since 21st Century, Chinese paper cutting has been recognized as an important element of Intangible Cultural Heritage (ICH) of China, which served as a symbol of traditional aesthetics, cultural identity and social values. Due to their rich cultural importance, the digital protection of paper cutting artwork is crucial. Moreover, existing approaches such as Progressive Conditional Generative Adversarial Network with Archimedes Optimization Algorithm (PCGAN-AOA) struggle from generalizability, due to lack of ability to capture long range dependencies in paper cutting patterns. Hence, A hybrid novel approach namely, PCGAN with Vision Transformer (PCGAN-ViT) is proposed for digital protection of ICH and inheritance of Chinese paper cutting. Initially, the input data consists of complete paper cutting pattern images which are gathered from multiple sources such as local cultural heritage institutions, museums, and verified public repositories. Then these images are preprocessed by performing resizing through bilinear interpolation and normalization of pixels through min max scaling. Lastly, these preprocessed images are fed to the proposed PCGAN-ViT model, where PCGAN performs the generation of realistic samples and ViT is used to verify the stylistic correctness by classifying the generated patterns. Therefore, the proposed PCGAN-ViT achieved low Root Mean Squared Error (RMSE) of 0.097 which outperformed existing PCGAN-AOA model.