An open dataset for oracle bone script recognition and decipherment
원문 초록
Oracle bone script, one of the earliest known forms of ancient Chinese writing, presents invaluable research materials for scholars studying 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 deciphering Oracle Bone Characters (OBCs) 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-OBC dataset. This dataset encompasses 77,064 images of 1,588 individual deciphered characters and 62,989 images of 9,411 undeciphered characters, with a total of 140,053 images, compiled from diverse sources. The hope is that this dataset could inspire and assist future research in deciphering those unknown OBCs. All the codes and datasets are available at https://github.com/Yuliang-Liu/Open-Oracle.
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OracleFusion: Assisting the Decipherment of Oracle Bone Script with Structurally Constrained Semantic Typography
Deciphering Oracle Bone Language with Diffusion Models
Deciphering Undersegmented Ancient Scripts Using Phonetic Prior
Phonetic and Visual Priors for Decipherment of Informal Romanization
A Probabilistic Formulation of Unsupervised Text Style Transfer
Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B
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