OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions

• URL: http://arxiv.org/abs/2504.13524v1
• Date: Fri, 18 Apr 2025 07:24:35 GMT
• Title: OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions
• Authors: Jinhao Li, Zijian Chen, Tingzhu Chen, Zhiji Liu, Changbo Wang,
• Abstract summary: Oracle bone inscriptions (OBIs) are the earliest known form of Chinese characters and serve as a valuable resource for research in anthropology and archaeology.<n>Previous methods either focus on pixel-level information or utilize vanilla transformers for glyph-based OBI denoising.<n>This paper proposes a fast attentive denoising framework for oracle bone inscriptions, i.e., OBIFormer.
• Score: 7.657419462547438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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