Prism-OBI: a novel framework for oracle bone inscription recognition via visual perception and feature decoupling
Source abstract
Abstract
Abstract Oracle bone inscriptions (OBIs) play a vital role in the digital preservation of human civilization, offering profound historical insights. However, its recognition faces significant challenges due to severe degradations, such as blurred strokes and complex geological noise, which generic end-to-end models struggle to disentangle. To address these issues, we propose Prism-OBI, an innovative two-stage, decoupled framework that explicitly separates spatial localization from fine-grained semantic classification. Specifically, we present a degradation-aware visual perception methodology that leverages signal decoupling, dynamic scale adaptation, and explicit spatial encoding to serve as a semantic filter, effectively isolating coherent strokes from background artifacts. Extensive experiments demonstrate that this decoupled paradigm mitigates the localization-classification trade-off, laying a solid foundation for the transferable analysis of diverse historical archives in digital humanities.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Processing and 3D Reconstruction · Handwritten Text Recognition Techniques · Digital Humanities and Scholarship
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (60)
Show source-provided references
1. Diao, X. et al. Oracle bone inscription image restoration via glyph extraction. NPJ Herit. Sci. 13, 321 (2025).10.1038/s40494-025-01795-8 ↗
2. Wang, S., Yu, Q., Wang, Y., Liu, D. & Li, X. Unsupervised GAN-based model for super-resolution of oracle bone rubbing images. NPJ Herit. Sci. 13, 584 (2025).10.1038/s40494-025-02154-3 ↗
3. Yue, X., Li, H., Fujikawa, Y. & Meng, L. Dynamic dataset augmentation for deep learning-based oracle bone inscriptions recognition. ACM J. Comput. Cult. Herit. 15, 76 (2022).
4. Memon, J., Sami, M., Khan, R. A. & Uddin, M. Handwritten optical character recognition (OCR): a comprehensive systematic literature review (SLR). IEEE Access 8, 142642–142668 (2020).10.1109/ACCESS.2020.3012542 ↗
5. Dress, A., Grünewald, S. & Zeng, Z. A cognitive network for oracle bone characters related to animals. Int. J. Mod. Phys. B 30, 1630001 (2016).10.1142/S0217979216300012 ↗
6. Feng, G., Jing, X. & Liu, Y. G. Recognition of fuzzy characters on oracle-bone inscriptions. In Proceedings of the 2015 IEEE International Conference on Computer and Information Technology, 698–702 (IEEE, 2015).10.1109/CIT/IUCC/DASC/PICOM.2015.101 ↗
7. Li, Q., Wu, Q. & Yang, Y. Dynamic description library for jiaguwen characters and the research of the characters processing. Acta Sci. Nat. Univ. Pekin. 49, 61–67 (2013).
8. Lu, X., Li, M., Cai, K., Wang, X. & Tang, Y. A graphic-based method for Chinese oracle-bone classification. J. Beijing Inf. Sci. Technol. Univ. 25, 92–96 (2010).
9. Meng, L. Two-stage recognition for oracle bone inscriptions. In Proceedings of the International Conference on Image Analysis and Processing, 672–682 (Springer, 2017).10.1007/978-3-319-68548-9_61 ↗
10. Bathula, A. et al. Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review. Artif. Intell. Rev. 57, 238 (2024).10.1007/s10462-024-10873-5 ↗
11. Merugu, S., Reddy, M. C. S., Goyal, E. & Piplani, L. Text message classification using supervised machine learning algorithms. In Proceedings of the International Conference on Communications and Cyber Physical Engineering, 141–150 (Springer, 2018).10.1007/978-981-13-0212-1_15 ↗
12. Haq, M. A. & Rahim Khan, M. A. DNNBoT: deep neural network-based botnet detection and classification. Comput. Mater. Contin. 71, 1729–1750 (2022).
13. Merugu, S., Yadav, R., Pathi, V. & Perianayagam, H. R. Identification and improvement of image similarity using autoencoder. Eng. Technol. Appl. Sci. Res. 14, 15541–15546 (2024).10.48084/etasr.7548 ↗
14. Huang, Y. et al. An improved hybrid CNN-LSTM-Attention model with Kepler optimization algorithm for wind speed prediction. Eng. Lett. 32, 1957–1965 (2024).
15. Guo, J., Wang, C., Roman-Rangel, E., Chao, H. & Rui, Y. Building hierarchical representations for oracle character and sketch recognition. IEEE Trans. Image Process. 25, 104–118 (2015).10.1109/TIP.2015.2500019 ↗
16. Fu, X., Yang, Z., Zeng, Z., Zhang, Y. & Zhou, Q. Improvement of oracle bone inscription recognition accuracy: a deep learning perspective. ISPRS Int. J. Geo-Inf. 11, 45 (2022).10.3390/ijgi11010045 ↗
17. Song, Y., Zhang, C. & Wu, M. YOLO-ACD: Robust lightweight detector for ancient character detection. In Proceedings of the 5th International Conference on Signal Processing and Machine Learning, 177–186 (IET, 2025).10.54254/2755-2721/2025.20928 ↗
18. Xu, H., Zhang, Z., Liang, Q. & Lin, Z. Research on the intelligent oracle bone script recognition model based on Otsu and improved YOLOv8. In Proceedings of the 2024 IEEE 4th International Conference on Electronic Technology, Communication and Information, 972–976 (IEEE, 2024).10.1109/ICETCI61221.2024.10594291 ↗
19. Zhen, Q., Wu, L. & Liu, G. An oracle bone inscriptions detection algorithm based on improved YOLOv8. Algorithms 17, 174 (2024).10.3390/a17050174 ↗
20. Xing, J., Liu, G. & Xiong, J. Oracle bone inscription detection: a survey of oracle bone inscription detection based on deep learning algorithm. In Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, 1–8 (ACM, 2019).10.1145/3371425.3371434 ↗
21. Huang, S., Wang, H., Liu, Y., Shi, X. & Jin L. OBC306: A large-scale oracle bone character recognition dataset. In Proceedings of the 2019 International Conference on Document Analysis and Recognition, 681–688 (IEEE, 2019).10.1109/ICDAR.2019.00114 ↗
22. Li, D. & Du, B. Research on oracle bone inscription segmentation and recognition model based on deep learning. In Proceedings of the 2024 IEEE 4th International Conference on Electronic Technology, Communication and Information, 1309–1314 (IEEE, 2024).10.1109/ICETCI61221.2024.10594164 ↗
23. Wang, N., Qi, Z. & Du, F. Research on oracle bone inscriptions recognition from multi-source data based on deep learning. In Proceedings of the 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference, 290–293 (IEEE, 2024).10.1109/IAEAC59436.2024.10503753 ↗
24. Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: unified, real-time object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 779–788 (IEEE, 2016).10.1109/CVPR.2016.91 ↗
25. Redmon, J. & Farhadi, A. YOLO9000: better, faster, stronger. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7263–7271 (IEEE, 2017).10.1109/CVPR.2017.690 ↗
26. Redmon, J. & Farhadi, A. YOLOv3: an incremental improvement. Preprint at https://arxiv.org/abs/1804.02767 (2018).
27. Khanam, R. & Hussain, M. YOLOv11: an overview of the key architectural enhancements. Preprint at https://arxiv.org/abs/2410.17725 (2024).
28. Wang, C. Y., Bochkovskiy, A. & Liao, H. Y. M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7464–7475 (IEEE, 2023).10.1109/CVPR52729.2023.00721 ↗
29. Murugan, B. & Visalakshi, P. GAN augmented hybrid transformer network (GHTNet) for ancient Tamil stone inscription recognition. NPJ Herit. Sci. 13, 604 (2025).10.1038/s40494-025-02097-9 ↗
30. Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7132–7141 (IEEE, 2018).10.1109/CVPR.2018.00745 ↗
31. Chen, J., Mai, H., Luo, L., Chen, X. & Wu, K. Effective feature fusion network in BiFPN for small object detection. In Proceedings of the 2021 IEEE International Conference on Image Processing, 699–703 (IEEE, 2021).10.1109/ICIP42928.2021.9506347 ↗
32. Hou, Q., Zhou, D. & Feng, J. Coordinate attention for efficient mobile network design. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13713–13722 (IEEE, 2021).10.1109/CVPR46437.2021.01350 ↗
33. Li, S., Nguyen, L. T., Chansanam, W., Iam-On, N. & Boongoen, T. Enhancing Dongba pictograph recognition using convolutional neural networks and data augmentation techniques. Information 16, 362 (2025).10.3390/info16050362 ↗
34. Mai, C., Penava, P. & Buettner, R. Oracle bone inscription character recognition based on a novel convolutional neural network architecture. IEEE Access 12, 197021–197034 (2024).10.1109/ACCESS.2024.3521319 ↗
35. Wang, M., Deng, W. & Liu, C. L. Unsupervised structure-texture separation network for oracle character recognition. IEEE Trans. Image Process. 31, 3137–3150 (2022).10.1109/TIP.2022.3165989 ↗
36. Wang, M. et al. Study on the evolution of Chinese characters based on few-shot learning: from oracle bone inscriptions to regular script. PLoS ONE 17, e0272974 (2022).10.1371/journal.pone.0272974 ↗
37. Wei, X. S. et al. Fine-grained image analysis with deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44, 8927–8948 (2021).10.1109/TPAMI.2021.3126648 ↗
38. Zhang, H. et al. ResNeSt: split-attention networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2736–2746 (IEEE, 2022).10.1109/CVPRW56347.2022.00309 ↗
39. Borisov, V. et al. Deep neural networks and tabular data: a survey. IEEE Trans. Neural Netw. Learn. Syst. 35, 7499–7519 (2022).10.1109/TNNLS.2022.3229161 ↗
40. Maurício, J., Domingues, I. & Bernardino, J. Comparing Vision Transformers and convolutional neural networks for image classification: a literature review. Appl. Sci. 13, 5521 (2023).10.3390/app13095521 ↗
41. Song, Y., Wang, T., Cai, P., Mondal, S. K. & Sahoo, J. P. A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. ACM Comput. Surv. 55, 1–40 (2023).10.1145/3582688 ↗
42. Xing, J., Liu, G. & Xiong, J. Oracle bone inscription detection: a survey of oracle bone inscription detection based on deep learning algorithm. In Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC), 1–8 (ACM, 2019).10.1145/3371425.3371434 ↗
43. Liu, G., Chen, S., Xiong, J. & Jiao, Q. An oracle bone inscription detector based on multi-scale Gaussian kernels. Appl. Math. 12, 224–239 (2021).10.4236/am.2021.123014 ↗
44. Zhou, S., Wang, X., Qiu, J., Bu, W. & Wang, H. OracleNet: enhancing oracle bone script recognition with adaptive deformation and texture-structure decoupling. NPJ Herit. Sci. 13, 273 (2025).10.1038/s40494-025-01839-z ↗
45. Li, J. et al. A resource-efficient multi-entropy fusion method and its application for EEG-based emotion recognition. Entropy 27, 96 (2025).10.3390/e27010096 ↗
46. Liu, S. et al. Hierarchical cascaded networks with multi-task balanced loss for fine-grained hashing. Eng. Appl. Artif. Intell. 152, 10659 (2025).10.1016/j.engappai.2025.110659 ↗
47. Chen, R. et al. Large-scale cross-modal hashing via Kolmogorov-Arnold representation theorem and optimal transport. Knowl.-Based Syst. 330, 114698 (2025).10.1016/j.knosys.2025.114698 ↗
48. Chen, R. et al. A smooth distributed and multi-objective ADP-based power system optimal operation control method. Sustain. Energy Technol. Assess. 87, 104903 (2026).
49. Varghese, R. & Sambath, M. YOLOv8: a novel object detection algorithm with enhanced performance and robustness. In Proceedings of the 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), 1–6 (IEEE, 2024).10.1109/ADICS58448.2024.10533619 ↗
50. Meng, L. et al. Oracle bone inscription detector based on SSD. In Proceedings of the International Conference on Image Analysis and Processing, 126–136 (Springer, 2019).10.1007/978-3-030-30754-7_13 ↗
51. Ren, S., He, K., Girshick, R., Sun, J. & Faster, R.-C. N. N. Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2016).10.1109/TPAMI.2016.2577031 ↗
52. Zhang, S. et al. Single-shot refinement neural network for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4203–4212 (IEEE, 2018).10.1109/CVPR.2018.00442 ↗
53. Fu, X., Zhou, R., Yang, X. & Li, C. Detecting oracle bone inscriptions via pseudo-category labels. Herit. Sci. 12, 107 (2024).10.1186/s40494-024-01221-5 ↗
54. Cui, J., Liu, S., Tian, Z., Zhong, Z. & Jia, J. ResLT: residual learning for long-tailed recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45, 3695–3706 (2022).
55. Zhang, Y., Hooi, B., Hong, L. & Feng, J. Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. Adv. Neural Inf. Process. Syst. 35, 34077–34090 (2022).
56. Li, J. et al. Towards better long-tailed oracle character recognition with adversarial data augmentation. Pattern Recognit. 140, 109534 (2023).10.1016/j.patcog.2023.109534 ↗
57. Zhuo, S. & Zhang, J. Attention-based deformable convolutional network for Chinese various dynasties character recognition. Expert Syst. Appl. 238, 121881 (2024).10.1016/j.eswa.2023.121881 ↗
58. Zhou, Y. et al. UFCNet: unsupervised network based on Fourier transform and convolutional attention for oracle character recognition. In Proceedings of the 1st Workshop on Machine Learning for Ancient Languages, 98–106 (ACL, 2024).10.18653/v1/2024.ml4al-1.11 ↗
59. Wang, M., Deng, W. & Su, S. Oracle character recognition using unsupervised discriminative consistency network. Pattern Recognit. 148, 110180 (2024).10.1016/j.patcog.2023.110180 ↗
60. Hu, D. & Qin, Y. A deep network based on dual attention for oracle bone inscription character recognition. In Proceedings of the 2024 4th International Conference on Communication Technology and Information Technology (ICCTIT), 706–709 (IEEE, 2024).10.1109/ICCTIT64404.2024.10928348 ↗