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 (No. 62272058)

Received Date: 2026-03-23
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 severely hindered by extreme long-tailed distributions and large intra-class variances. Conventional deep learning methods are easily dominated by head classes, whereas existing methods often over-fit tail classes or ignore intrinsic learning difficulties. To address these bottlenecks, a two-stage decoupled learning framework is proposed to improve the recognition of tail and difficult classes while preserving discriminative power for head classes. Methods The framework separates representation learning from classifier optimization. In the first stage, the backbone is trained using mixed augmentation (CutMix and RandAug) and label-distribution-aware margin (LDAM) loss to extract robust features and mitigate intra-class variance. In the second stage, the backbone is frozen. The classifier is optimized utilizing a novel class-difficulty-based adaptive sampling strategy, which dynamically allocates sampling weights based on historical training loss, coupled with a class-balanced loss (CBL) to refine decision boundaries. 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%. It significantly outperforms the Inception-v4 baseline by 19.61% in average class accuracy. Comprehensive ablation studies validatethat the difficulty sampling strategy successfully boosts the recognition of rare and challenging characters. Additionally, sensitivity and qualitative analyses further confirm the robustness and interpretability of the model. Conclusions The proposed decoupled learning algorithm effectively addresses long-tailed OBC recognition by balancing learning priorities across diverse classes. Mixed augmentation enriches sample diversity, while difficulty-aware sampling refines decision boundaries for complex tail characters without sacrificing head class performance. This framework provides a highly robust and scalable solution for the digital recognition of ancient scripts and archaeological AI applications.
- Oracle Bone Character recognition,
- Long-tailed distribution,
- Adaptive difficulty sampling,
- Decoupled learning

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References(25)

References

[1]	葛亮. 一百二十年来甲骨文材料的初步统计[J]. 汉字汉语研究, 2019(4): 33–54,125. doi: 10.13513/j.cnki.41-1041/h.2019.04.006. GE Liang. Preliminary statistics of inscribed oracle bones excavated in the past 120 years[J]. The Study of Chinese Characters and Language, 2019(4): 33–54,125. doi: 10.13513/j.cnki.41-1041/h.2019.04.006.
[2]	GUO Jun, WANG Changhu, ROMAN-RANGEL E, et al. Building hierarchical representations for oracle character and sketch recognition[J]. IEEE Transactions on Image Processing, 2016, 25(1): 104–118. doi: 10.1109/TIP.2015.2500019.
[3]	ZHANG Yikang, ZHANG Heng, LIU Yongge, et al. Oracle character recognition by nearest neighbor classification with deep metric learning[C]. Proceedings of 2019 International Conference on Document Analysis and Recognition, Sydney, Australia, 2019: 309–314. doi: 10.1109/ICDAR.2019.00057.
[4]	HUANG Shuangping, WANG Haobin, LIU Yongge, et al. OBC306: A large-scale oracle bone character recognition dataset[C]. Proceedings of International Conference on Document Analysis and Recognition, Sydney, Australia, 2019: 681–688. doi: 10.1109/ICDAR.2019.00114.
[5]	GUAN Haisu, WAN Jinpeng, LIU Yuliang, et al. An open dataset for the evolution of oracle bone characters: EVOBC[J]. arXiv preprint arXiv: 2401.12467, 2024. (查阅网上资料, 不确定文献类型及格式是否正确, 请确认).
[6]	韩佳艺, 刘建伟, 陈德华, 等. 深度长尾学习研究综述[J]. 自动化学报, 2025, 51(5): 985–1020. doi: 10.16383/j.aas.c240077. HAN Jiayi, LIU Jianwei, CHEN Dehua, et al. Survey on deep long-tailed learning[J]. Acta Automatica Sinica, 2025, 51(5): 985–1020. doi: 10.16383/j.aas.c240077.
[7]	LI Jing, WANG Qiufeng, ZHANG Rui, et al. Mix-up augmentation for oracle character recognition with imbalanced data distribution[C]. Proceedings of the 16th International Conference on Document Analysis and Recognition, Lausanne, Switzerland, 2021: 237–251. doi: 10.1007/978-3-030-86549-8_16.
[8]	HUANG Hongxiang, YANG Daihui, DAI Gang, et al. AGTGAN: Unpaired image translation for photographic ancient character generation[C]. Proceedings of the 30th ACM International Conference on Multimedia, Lisboa, Portugal, 2022: 5456–5467. doi: 10.1145/3503161.3548338.
[9]	LI Jing, WANG Qiufeng, WANG Siyuan, et al. Diff-Oracle: Deciphering oracle bone scripts with controllable diffusion model[J]. arXiv preprint arXiv: 2312.13631, 2024. (查阅网上资料, 不确定文献类型及格式是否正确, 请确认).
[10]	LI Jing, DONG Bin, WANG Qiufeng, et al. Decoupled learning for long-tailed oracle character recognition[C]. Proceedings of the 17th International Conference on Document Analysis and Recognition, San José, USA, 2023: 165–181. doi: 10.1007/978-3-031-41685-9_11.
[11]	YUN S, HAN D, CHUN S, et al. CutMix: Regularization strategy to train strong classifiers with localizable features[C]. Proceedings of International Conference on Computer Vision, Seoul, South Korea, 2019: 6022–6031. doi: 10.1109/ICCV.2019.00612.
[12]	CUBUK E D, ZOPH B, SHLENS J, et al. Randaugment: Practical automated data augmentation with a reduced search space[C]. Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, USA, 2020: 3008–3017. doi: 10.1109/CVPRW50498.2020.00359.
[13]	LI Jing, CHI Xueke, WANG Qiufeng, et al. A comprehensive survey of oracle character recognition: Challenges, datasets, methodology, and beyond[J]. Pattern Recognition, 2026, 169: 111824. doi: 10.1016/j.patcog.2025.111824.
[14]	ZHOU Xinlun, HUA Xingcheng, and LI Feng. A method of Jia Gu Wen recognition based on a two-level classification[C]. Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, Canada, 1995: 833–836. doi: 10.1109/ICDAR.1995.602030.
[15]	栗青生, 杨玉星, 王爱民. 甲骨文识别的图同构方法[J]. 计算机工程与应用, 2011, 47(8): 112–114. doi: 10.3778/j.issn.1002-8331.2011.08.033. LI Qingsheng, YANG Yuxing, and WANG Aimin. Recognition of inscriptions on bones or tortoise shells based on graph isomorphism[J]. Computer Engineering and Applications, 2011, 47(8): 112–114. doi: 10.3778/j.issn.1002-8331.2011.08.033.
[16]	SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]. Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, USA, 2017: 4278–4284. doi: 10.1609/aaai.v31i1.11231.
[17]	MAI C, PENAVA P, and BUETTNER R. Oracle bone inscription character recognition based on a novel convolutional neural network architecture[J]. IEEE Access, 2024, 12: 197021–197034. doi: 10.1109/ACCESS.2024.3521319.
[18]	毕晓君, 毛亚菲. 基于监督对比学习的小样本甲骨文字识别[J]. 智能系统学报, 2024, 19(1): 106–113. doi: 10.11992/tis.202309008. BI Xiaojun and MAO Yafei. Few-shot oracle bone character recognition based on supervised contrastive learning[J]. CAAI Transactions on Intelligent Systems, 2024, 19(1): 106–113. doi: 10.11992/tis.202309008.
[19]	刘宗昊, 彭文杰, 代港, 等. 语义增强的零样本甲骨文字符识别[J]. 电子学报, 2024, 52(10): 3347–3358. doi: 10.12263/DZXB.20240286. LIU Zonghao, PENG Wenjie, DAI Gang, et al. Semantic-enhanced zero-shot oracle character recognition[J]. Acta Electronica Sinica, 2024, 52(10): 3347–3358. doi: 10.12263/DZXB.20240286.
[20]	WANG Wei, ZHANG Ting, ZHAO Yiwen, et al. Improving oracle bone characters recognition via a CycleGAN-based data augmentation method[C]. Proceedings of the 29th International Conference on Neural Information Processing, Changsha, China, 2022: 88–100. doi: 10.1007/978-981-99-1645-0_8. (查阅网上资料,未找到本条文献出版地信息,请确认).
[21]	LI Jing, WANG Qiufeng, HUANG Kaizhu, et al. Towards better long-tailed oracle character recognition with adversarial data augmentation[J]. Pattern Recognition, 2023, 140: 109534. doi: 10.1016/j.patcog.2023.109534.
[22]	CAO Kaidi, WEI C, GAIDON A, et al. Learning imbalanced datasets with label-distribution-aware margin loss[C]. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, Canada, 2019: 140.
[23]	YU Sihao, GUO Jiafeng, ZHANG Ruqing, et al. A re-balancing strategy for class-imbalanced classification based on instance difficulty[C]. Proceedings of Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 70–79. doi: 10.1109/CVPR52688.2022.00017.
[24]	CUI Yin, JIA Menglin, LIN T Y, et al. Class-balanced loss based on effective number of samples[C]. Proceedings of Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 9260–9269. doi: 10.1109/CVPR.2019.00949.
[25]	YU Weihao, ZHOU Pan, YAN Shuicheng, et al. InceptionNeXt: When inception meets ConvNeXt[C]. Proceedings of Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2024: 5672–5683. doi: 10.1109/CVPR52733.2024.00542.