  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.
