In China, through investigation, it has been determined that Qin bamboo slips unearthed as artifacts are severely damaged, deformed, and corroded. The sluggish advancement in Qin bamboo slip inpainting has prompted the exploration of artificial intelligence applications in the domain of image text, offering a promising avenue for the automated restoration of ancient texts. This paper proposes an improved context encoder to restore missing parts in the Qin bamboo slip character images. An encoder can be used to process images, while another encoder can handle text problems, and the encoded representations from both can be combined to generate answers. Additionally, in generative adversarial networks, using two encoders can enhance the performance of both the generator and discriminator, improving training stability. While one encoder encodes the input data into latent space, the discriminator employs the other encoder to improve discrimination between real and generated samples, thereby elevating generation quality and training stability.
This paper presents a generative adversarial network (GAN)-based algorithm for generating Qin bamboo slips character images, specifically addressing the issues of limited samples and a high occurrence of fragmented characters. To mitigate the interference caused by the image background on the network, a global thresholding binary segmentation method is employed to separate the foreground and background of Qin bamboo slips character images. Additionally, we propose the SwinGAN network model based on the DCGAN architecture. The SwinGAN generator network incorporates a windowed multi-head attention mechanism and a Qin Transformer module that combines convolutional neural networks. To prevent gradient varnish, spectral normalization is applied to the convolutional layers of the discriminator, constraining the weight variations. Furthermore, to ensure stable model training, the Wasserstein distance is adopted as the objective function to measure the difference between the generated data distribution and the real data distribution.
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