Originating from China's Shang Dynasty approximately 3,000 years ago, the
Oracle Bone Script (OBS) is a cornerstone in the annals of linguistic history,
predating many established writing systems. Despite the discovery of thousands
of inscriptions, a vast expanse of OBS remains undeciphered, casting a veil of
mystery over this ancient language. The emergence of modern AI technologies
presents a novel frontier for OBS decipherment, challenging traditional NLP
methods that rely heavily on large textual corpora, a luxury not afforded by
historical languages. This paper introduces a novel approach by adopting image
generation techniques, specifically through the development of Oracle Bone
Script Decipher (OBSD). Utilizing a conditional diffusion-based strategy, OBSD
generates vital clues for decipherment, charting a new course for AI-assisted
analysis of ancient languages. To validate its efficacy, extensive experiments
were conducted on an oracle bone script dataset, with quantitative results
demonstrating the effectiveness of OBSD. Code and decipherment results will be
made available at this https URL

通过采用图像生成技术，本文引入了一种新颖的方法，即通过开发 Oracle Bone Script Decipher (OBSD)—— 一种基于条件扩散策略的图像生成方法，为 Oracle Bone Script（OBS）解密提供重要线索，为古代语言的人工智能辅助分析开辟了新的道路。

扩散模型解读甲骨文语言

Deciphering Oracle Bone Language with Diffusion Models

Oracle Bone Script (OBS), one of the earliest known forms of ancient Chinese
writing, holds invaluable insights into the humanities and geography of the
Shang Dynasty, dating back 3,000 years. The immense historical and cultural
significance of these writings cannot be overstated. However, the passage of
time has obscured much of their meaning, presenting a significant challenge in
deciphering these ancient texts. With the advent of Artificial Intelligence
(AI), employing AI to assist in interpreting OBS has become a feasible option.
Yet, progress in this area has been hindered by a lack of high-quality
datasets. To address this issue, this paper details the creation of the
HUST-OBS dataset. This dataset encompasses 77,064 images of 1,588 individual
deciphered scripts and 62,989 images of 9,411 undeciphered characters, with a
total of 140,053 images, compiled from diverse sources. Additionally, all
images and labels have been reviewed and corrected by experts in oracle bone
studies. The hope is that this dataset could inspire and assist future research
in deciphering those unknown OBS.

创造了华中科技大学甲骨学数据集，包括 77,064 张已解码甲骨文字图像和 62,989 张未解码字符的图像，共计 140,053 张图像，为未知甲骨文字的解码研究提供了帮助。