Decoupled Learning for Long-tailed Oracle Bone Character Recognition Based on Adaptive Difficulty Sampling

SUN Junwei; GUAN Suyan; CHEN Xinyu; WANG Kun; CAI Yuanqiang

doi:10.11999/JEIT260327

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SUN Junwei, GUAN Suyan, CHEN Xinyu, WANG Kun, CAI Yuanqiang. Decoupled Learning for Long-tailed Oracle Bone Character Recognition Based on Adaptive Difficulty Sampling[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260327

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SUN Junwei, GUAN Suyan, CHEN Xinyu, WANG Kun, CAI Yuanqiang. Decoupled Learning for Long-tailed Oracle Bone Character Recognition Based on Adaptive Difficulty Sampling[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260327

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Decoupled Learning for Long-tailed Oracle Bone Character Recognition Based on Adaptive Difficulty Sampling

doi: 10.11999/JEIT260327 cstr: 32379.14.JEIT260327

School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, 100876, China

Funds: The National Natural Science Foundation of China (62272058)

Received Date: 2026-03-25
Accepted Date: 2026-06-29
Rev Recd Date: 2026-06-28

Available Online: 2026-07-12

Abstract

Abstract

Objective Oracle Bone Character (OBC) recognition is challenged by an extreme long-tailed distribution and substantial intra-class variation. Conventional deep learning methods are often dominated by head classes, whereas existing approaches tend to overfit tail classes or fail to account for differences in learning difficulty across classes. To address these limitations, a two-stage decoupled learning framework is proposed to improve the recognition of tail and difficult classes while preserving the discriminative capability of head classes. Methods The proposed framework decouples feature representation learning from classifier optimization. In the first stage, the backbone network is trained using a mixed data augmentation strategy that combines CutMix and RandAugment with Label-Distribution-Aware Margin (LDAM) loss to learn robust feature representations and alleviate the effect of intra-class variation. In the second stage, the backbone network is frozen, and only the classifier is optimized. An adaptive difficulty sampling strategy is proposed to dynamically assign sampling weights according to historical and current class-level training difficulty. The classifier is further optimized using a Class-Balanced LDAM (CBL) loss, which combines class-balanced weighting with LDAM to refine decision boundaries for long-tailed classification. Results and Discussions Experiments on the highly imbalanced OBC306 dataset demonstrate that the proposed method achieves an overall accuracy of 94.34% and an average class accuracy of 89.89%. Compared with the Inception-v4 baseline, the proposed method improves the average class accuracy by 19.61%. Comparisons with representative long-tailed OBC recognition methods further demonstrate superior overall performance. Comprehensive ablation studies verify the effectiveness of the mixed data augmentation strategy and the adaptive difficulty sampling strategy in improving the recognition of rare and difficult characters. Parameter sensitivity analysis and qualitative error analysis further confirm the robustness and effectiveness of the proposed framework. Conclusions The proposed two-stage decoupled learning framework effectively addresses long-tailed OBC recognition by balancing the learning priorities of head, tail, and difficult classes. The mixed data augmentation strategy improves feature robustness, whereas the adaptive difficulty sampling strategy and the Class-Balanced LDAM loss jointly optimize classifier learning and refine decision boundaries without degrading head-class recognition performance. The proposed framework provides an effective solution for the digital recognition of Oracle Bone Characters and offers technical support for low-resource ancient character recognition.
- Oracle Bone Character(OBC) recognition,
- Long-tailed distribution,
- Adaptive difficulty sampling,
- Decoupled learning

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References(25)

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