OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions
Journal:
arXiv
Published Date:
Apr 18, 2025
Abstract
Oracle bone inscriptions (OBIs) are the earliest known form of Chinese
characters and serve as a valuable resource for research in anthropology and
archaeology. However, most excavated fragments are severely degraded due to
thousands of years of natural weathering, corrosion, and man-made destruction,
making automatic OBI recognition extremely challenging. Previous methods either
focus on pixel-level information or utilize vanilla transformers for
glyph-based OBI denoising, which leads to tremendous computational overhead.
Therefore, this paper proposes a fast attentive denoising framework for oracle
bone inscriptions, i.e., OBIFormer. It leverages channel-wise self-attention,
glyph extraction, and selective kernel feature fusion to reconstruct denoised
images precisely while being computationally efficient. Our OBIFormer achieves
state-of-the-art denoising performance for PSNR and SSIM metrics on synthetic
and original OBI datasets. Furthermore, comprehensive experiments on a real
oracle dataset demonstrate the great potential of our OBIFormer in assisting
automatic OBI recognition. The code will be made available at
https://github.com/LJHolyGround/OBIFormer.
