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<p>Qin and Han bamboo slips (221 BCE–220 CE), as invaluable relics of Chinese civilization, have suffered significant degradation over time. The lack of training datasets presents challenges for character restoration. To address this, a dedicated dataset for text recognition was constructed in collaboration with ancient script experts. An improved conditional generative adversarial network (GAN) with an effective character contour length (ECCL) constraint is proposed to restore degraded characters. The method integrates UNet and ResNet architectures in the generator to process damaged slip images and output restored text, while a multi-layer convolutional discriminator distinguishes between real and generated data. The ECCL constraint is introduced into the generator’s loss function to enhance noise removal and structural preservation. Comparative experiments with five baseline methods (AF-ECCL, CycleGAN, CID-GAN, Swin Transformer, pix2pix) demonstrate superior performance in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and ECCL Ratio, validating the effectiveness of the proposed approach.</p>

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Character restoration of Qin and Han bamboo slips based on improved conditional generative adversarial networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月18日

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  • Songxiao Cao,
  • Tianwei Pan,
  • Yuanyuan Wang,
  • Tao Song

摘要

Qin and Han bamboo slips (221 BCE–220 CE), as invaluable relics of Chinese civilization, have suffered significant degradation over time. The lack of training datasets presents challenges for character restoration. To address this, a dedicated dataset for text recognition was constructed in collaboration with ancient script experts. An improved conditional generative adversarial network (GAN) with an effective character contour length (ECCL) constraint is proposed to restore degraded characters. The method integrates UNet and ResNet architectures in the generator to process damaged slip images and output restored text, while a multi-layer convolutional discriminator distinguishes between real and generated data. The ECCL constraint is introduced into the generator’s loss function to enhance noise removal and structural preservation. Comparative experiments with five baseline methods (AF-ECCL, CycleGAN, CID-GAN, Swin Transformer, pix2pix) demonstrate superior performance in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and ECCL Ratio, validating the effectiveness of the proposed approach.