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。

一个用于甲骨文识别和解读的开放数据集

An open dataset for oracle bone script recognition and decipherment

The earliest extant Chinese characters originate from oracle bone
inscriptions, which are closely related to other East Asian languages. These
inscriptions hold immense value for anthropology and archaeology. However,
deciphering oracle bone script remains a formidable challenge, with only
approximately 1,600 of the over 4,500 extant characters elucidated to date.
Further scholarly investigation is required to comprehensively understand this
ancient writing system. Artificial Intelligence technology is a promising
avenue for deciphering oracle bone characters, particularly concerning their
evolution. However, one of the challenges is the lack of datasets mapping the
evolution of these characters over time. In this study, we systematically
collected ancient characters from authoritative texts and websites spanning six
historical stages: Oracle Bone Characters - OBC (15th century B.C.), Bronze
Inscriptions - BI (13th to 221 B.C.), Seal Script - SS (11th to 8th centuries
B.C.), Spring and Autumn period Characters - SAC (770 to 476 B.C.), Warring
States period Characters - WSC (475 B.C. to 221 B.C.), and Clerical Script - CS
(221 B.C. to 220 A.D.). Subsequently, we constructed an extensive dataset,
namely EVolution Oracle Bone Characters (EVOBC), consisting of 229,170 images
representing 13,714 distinct character categories. We conducted validation and
simulated deciphering on the constructed dataset, and the results demonstrate
its high efficacy in aiding the study of oracle bone script. This openly
accessible dataset aims to digitalize ancient Chinese scripts across multiple
eras, facilitating the decipherment of oracle bone script by examining the
evolution of glyph forms.

通过构建一个包含 13,714 个不同字符类别的含有 229,170 张图片的 EVolution Oracle Bone Characters (EVOBC) 数据集，本研究展示了该数据集在辅助研究甲骨文上的作用，通过研究字符形式的演变来帮助解读甲骨文。

一个用于甲骨文演化的开放数据集：EVOBC

An open dataset for the evolution of oracle bone characters: EVOBC

Deciphering the oracle bone script plays a significant role in Chinese
archaeology and philology. However, it is significantly challenging due to the
scarcity of oracle character images. To overcome this issue, we propose
Diff-Oracle, based on diffusion models (DMs), to generate sufficient
controllable oracle characters. In contrast to most DMs that rely on text
prompts, we incorporate a style encoder to control style information during the
generation process. This encoder extracts style prompts from existing oracle
character images, where style details are converted from a CLIP model into a
text embedding format. Inspired by ControlNet, we introduce a content encoder
to capture desired content information from content images, ensuring the
fidelity of character glyphs. To train Diff-Oracle effectively, we propose to
obtain pixel-level paired oracle character images (i.e., style and content
images) by a pre-trained image-to-image translation model. Extensive
qualitative and quantitative experiments conducted on two benchmark datasets,
Oracle-241 and OBC306, demonstrate that our Diff-Oracle outperforms existing
generative methods in terms of image generation, further enhancing recognition
accuracy. Source codes will be available.

利用扩散模型生成足够可控的甲骨文字，通过样式编码器和内容编码器控制风格和内容信息，有效提高图像生成和识别准确性。