Abstract Oracle Bone Inscriptions (OBIs) are the earliest mature Chinese writing system, irreplaceable for decoding ancient Chinese civilization. However, automated OBI recognition is severely hampered by a lack of annotated data, as surviving rubbings suffer from critical abrasion, fragmentation, and extreme scarcity. To address this challenge, we propose an unsupervised cross-domain OBI recognition framework that transfers glyph knowledge from labeled handwritten oracle characters to unlabeled scanned rubbings, reducing dependence on large-scale annotated rubbing data. Our framework is anchored by a Progressive Domain Adaptation Network (PDAN), built on feature-space linear interpolation and a dynamically-weighted gradient inversion mechanism, paired with a structure-aware target-domain augmentation strategy. We also propose UDA-HS-1K, a large-scale benchmark dataset for the field. Comprehensive experiments show that our method achieves state-of-the-art performance, outperforming previous work on Oracle-241 (74.5% vs. 61.8%) and UDA-HS-1K (43.5% vs. 38.4%), validating the effectiveness of our method and the practical utility of our dataset.
(54032) et al. (Thu,) studied this question.
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