Cultural heritage serves as the enduring record of human thought and history.
Despite significant efforts dedicated to the preservation of cultural relics,
many ancient artefacts have been ravaged irreversibly by natural deterioration
and human actions. Deep learning technology has emerged as a valuable tool for
restoring various kinds of cultural heritages, including ancient text
restoration. Previous research has approached ancient text restoration from
either visual or textual perspectives, often overlooking the potential of
synergizing multimodal information. This paper proposes a novel Multimodal
Multitask Restoring Model (MMRM) to restore ancient texts, particularly
emphasising the ideograph. This model combines context understanding with
residual visual information from damaged ancient artefacts, enabling it to
predict damaged characters and generate restored images simultaneously. We
tested the MMRM model through experiments conducted on both simulated datasets
and authentic ancient inscriptions. The results show that the proposed method
gives insightful restoration suggestions in both simulation experiments and
real-world scenarios. To the best of our knowledge, this work represents the
pioneering application of multimodal deep learning in ancient text restoration,
which will contribute to the understanding of ancient society and culture in
digital humanities fields.

该研究论文探讨了文化遗产的保存与恢复问题，并提出了一种基于多模态深度学习的古代文字恢复模型（MMRM），通过结合上下文理解和损坏古代文物的残留视觉信息，实现同时预测损坏字符并生成恢复图像的功能。实验结果表明该方法在模拟实验和实际古代铭文中给出了有价值的恢复建议，为古代社会和文化的理解提供了贡献。