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<p>Oracle bone inscriptions(OBI) constitute a significant part of Chinese civilization, with many mysterious characters remaining undeciphered. Recent advances in deep learning have introduced novel approaches for OBI decipherment. However, OBI samples, often derived from tortoise shells and bones, are prone to corrosion and weathering, degrading dataset quality. This paper proposes an OBI augmentation model based on two-stage decomposition GANs, learning an unidirectional mapping to transform low-quality samples into high-quality ones. Unlike traditional data augmentation approaches that require pre-defined standard samples, our model utilizes a two-stage decomposition framework to augment OBI samples exclusively from existing data, ensuring enhanced realism and diversity, as measured by the Inception Score. Compared to existing augmentation techniques, our approach improves the Inception Score by 6.7% and reduces the Brisque Score by 30.2% on the public dataset. The augmented dataset quality improves recognition accuracy by 2.56%, demonstrating our approach’s effectiveness in real-world OBI decipherment.</p>

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Oracle bone heritage data augmentation based on two-stage decomposition GANs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月3日

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  • Jiaying Gao,
  • Fausto Giunchiglia,
  • Tongyu Zhao,
  • Xin Zhao,
  • Chuntao Li,
  • Hao Xu

摘要

Oracle bone inscriptions(OBI) constitute a significant part of Chinese civilization, with many mysterious characters remaining undeciphered. Recent advances in deep learning have introduced novel approaches for OBI decipherment. However, OBI samples, often derived from tortoise shells and bones, are prone to corrosion and weathering, degrading dataset quality. This paper proposes an OBI augmentation model based on two-stage decomposition GANs, learning an unidirectional mapping to transform low-quality samples into high-quality ones. Unlike traditional data augmentation approaches that require pre-defined standard samples, our model utilizes a two-stage decomposition framework to augment OBI samples exclusively from existing data, ensuring enhanced realism and diversity, as measured by the Inception Score. Compared to existing augmentation techniques, our approach improves the Inception Score by 6.7% and reduces the Brisque Score by 30.2% on the public dataset. The augmented dataset quality improves recognition accuracy by 2.56%, demonstrating our approach’s effectiveness in real-world OBI decipherment.