Oracle Bone Inscriptions Multi-modal Dataset
<|reference_start|>Oracle Bone Inscriptions Multi-modal Dataset: Oracle bone inscriptions(OBI) is the earliest developed writing system in China, bearing invaluable written exemplifications of early Shang history and paleography. However, the task of deciphering OBI, in the current climate of the scholarship, can prove extremely challenging. Out of the 4,500 oracle bone characters excavated, only a third have been successfully identified. Therefore, leveraging the advantages of advanced AI technology to assist in the decipherment of OBI is a highly essential research topic. However, fully utilizing AI's capabilities in these matters is reliant on having a comprehensive and high-quality annotated OBI dataset at hand whereas most existing datasets are only annotated in just a single or a few dimensions, limiting the value of their potential application. For instance, the Oracle-MNIST dataset only offers 30k images classified into 10 categories. Therefore, this paper proposes an Oracle Bone Inscriptions Multi-modal Dataset(OBIMD), which includes annotation information for 10,077 pieces of oracle bones. Each piece has two modalities: pixel-level aligned rubbings and facsimiles. The dataset annotates the detection boxes, character categories, transcriptions, corresponding inscription groups, and reading sequences in the groups of each oracle bone character, providing a comprehensive and high-quality level of annotations. This dataset can be used for a variety of AI-related research tasks relevant to the field of OBI, such as OBI Character Detection and Recognition, Rubbing Denoising, Character Matching, Character Generation, Reading Sequence Prediction, Missing Characters Completion task and so on. We believe that the creation and publication of a dataset like this will help significantly advance the application of AI algorithms in the field of OBI research.<|reference_end|>
@article{li2024oracle,
    title={Oracle Bone Inscriptions Multi-modal Dataset},
    author={Bang Li, Donghao Luo, Yujie Liang, Jing Yang, Zengmao Ding, Xu Peng,
  Boyuan Jiang, Shengwei Han, Dan Sui, Peichao Qin, Pian Wu, Chaoyang Wang, Yun
  Qi, Taisong Jin, Chengjie Wang, Xiaoming Huang, Zhan Shu, Rongrong Ji, Yongge
  Liu, Yunsheng Wu},
    journal={arXiv preprint arXiv:2407.03900},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03900},
    primaryClass={cs.CV}
}