Restoring Ancient Ideograph: A Multimodal Multitask Neural Network Approach

TL;DR

This paper introduces a novel multimodal multitask neural network that combines visual and textual data to improve the restoration of damaged ancient texts, especially ideographs, demonstrating promising results on both simulated and real datasets.

Contribution

It presents the first application of multimodal deep learning for ancient text restoration, integrating context and residual visual information for improved accuracy.

Findings

Effective restoration suggestions in simulated experiments

Successful application to authentic ancient inscriptions

Enhanced understanding of ancient texts through multimodal approach

Abstract

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.