Rejoining fragmented ancient bamboo slips with physics-driven deep learning

Abstract

Bamboo slips are a crucial medium for recording ancient civilizations in East Asia. However, many excavated bamboo slips have been fragmented into thousands of irregular pieces, making their rejoining a vital yet challenging step for understanding their content. Here we introduce WisePanda, a physics-driven deep learning framework designed to rejoin fragmented bamboo slips. Based on the physics of fracture and material deterioration, WisePanda automatically generates paired synthetic training data that captures the physical properties of bamboo fragmentations. This approach enables the training of a matching network without requiring manually paired samples, providing ranked suggestions to facilitate the rejoining process. Compared to the leading curve matching and modern generative methods, WisePanda yields a substantial and statistically significant increase in matching accuracy, accelerating archeologists’ efficiency in rejoining fragmented bamboo slips. WisePanda provides a new paradigm for addressing data scarcity in ancient artifact restoration through physics-driven machine learning.