Decoding Ancient Oracle Bone Script via Generative Dictionary Retrieval

TL;DR

This paper presents a deep learning-based, dictionary retrieval approach to deciphering ancient Oracle Bone Script, significantly improving accuracy and interpretability over previous methods.

Contribution

It introduces a novel dictionary-based retrieval framework guided by character evolution principles to decipher undeciphered ancient scripts.

Findings

Achieves 54.3% Top-10 accuracy on unseen characters

Achieves 86.6% Top-50 accuracy on unseen characters

Provides a scalable, interpretable method for archaeological decipherment

Abstract

Understanding humanity's earliest writing systems is crucial for reconstructing civilization's origins, yet many ancient scripts remain undeciphered. Oracle Bone Script (OBS) from China's Shang dynasty exemplifies this challenge: only approximately 1,500 of roughly 4,600 characters have been decoded, and a substantial portion of these 3,000-year-old inscriptions remains only partially understood. Limited by extreme data scarcity, existing computational methods achieve under 3% accuracy on unseen characters -- the core palaeographic challenge. We overcome this by reframing decipherment from classification to dictionary-based retrieval. Using deep learning guided by character evolution principles, we generate a comprehensive synthetic dictionary of plausible OBS variants for modern Chinese characters. Scholars query unknown inscriptions to retrieve visually similar candidates with transparent evidence, replacing algorithmic black boxes with interpretable hypotheses. Our approach achieves 54.3% Top-10 and 86.6% Top-50 accuracy for unseen characters. This scalable, transparent framework accelerates decipherment of a pivotal undeciphered script and establishes a generalizable methodology for AI-assisted archaeological discovery.