Abstract
Handwritten Chinese character recognition has achieved high accuracy using deep neural networks (DNNs), but the structural recognition (which offers structural interpretation, e.g., stroke and radical composition) is still a challenge. Existing DNNs treat character image as a whole and perform classification end-to-end without perception of the structure. They need a large amount of training samples to guarantee high generalization accuracy. In this paper, we propose a method for structural recognition of handwritten Chinese characters based on a modified part capsule auto-encoder (PCAE), which explicitly considers the hierarchical part-whole relationship of characters, and leverages extracted structural information for character recognition. Our PCAE is improved based on stacked capsule auto-encoder (SCAE) so as to better extract strokes and perform classification. By the modified PCAE, the character image is firstly decomposed into primitives (stroke segments), with their shape and pose information decoupled. The transformed primitives are aggregated into higher-level parts (strokes) guided by prior knowledge extracted from writing rules. This process enhances interpretability and improves the discrimination ability of features. Experimental results on a large dataset demonstrate the effectiveness of our method in both Chinese character recognition and stroke extraction tasks.
This work has been supported by the National Key Research and Development Program under Grant No. 2018AAA0100400, the National Natural Science Foundation of China (NSFC) grants 61836014 and U20A20223.
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References
Biederman, I.: Recognition-by-components: a theory of human image understanding. Psychol. Rev. 94(2), 115 (1987)
Chang, F.: Techniques for solving the large-scale classification problem in Chinese handwriting recognition. In: Doermann, D., Jaeger, S. (eds.) SACH 2006. LNCS, vol. 4768, pp. 161–169. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78199-8_10
Chiu, H.-P., Tseng, D.-C.: A novel stroke-based feature extraction for handwritten Chinese character recognition. Pattern Recogn. 32(12), 1947–1959 (1999)
Cireşan, D., Meier, U.: Multi-column deep neural networks for offline handwritten Chinese character classification. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2015)
Duarte, K., Rawat, Y., Shah, M.: VideoCapsuleNet: a simplified network for action detection. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Hahn, T., Pyeon, M., Kim, G.: Self-routing capsule networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Hinton, G.E., Sabour, S., Frosst, N.: Matrix capsules with EM routing. In: International Conference on Learning Representations (2018)
Jin, L.-W., Yin, J.-X., Gao, X., Huang, J.-C.: Study of several directional feature extraction methods with local elastic meshing technology for HCCR. In: Proceedings of the Sixth International Conference for Young Computer Scientist, pp. 232–236 (2001)
Kim, I.-J., Liu, C.-L., Kim, J.-H.: Stroke-guided pixel matching for handwritten Chinese character recognition. In: Proceedings of the Fifth International Conference on Document Analysis and Recognition, pp. 665–668. IEEE (1999)
Kosiorek, A., Sabour, S., Teh, Y.W., Hinton, G.E.: Stacked capsule autoencoders. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Lai, S., Jin, L., Yang, W.: Toward high-performance online HCCR: a CNN approach with DropDistortion, path signature and spatial stochastic max-pooling. Pattern Recogn. Lett. 89, 60–66 (2017)
Liu, C.-L., Yin, F., Wang, D.-H., Wang, Q.-F.: Online and offline handwritten Chinese character recognition: benchmarking on new databases. Pattern Recogn. 46(1), 155–162 (2013)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3856–3866 (2017)
Singh, M., Hoffman, D.D.: Part-based representations of visual shape and implications for visual cognition. In: Advances in Psychology, vol. 130, pp. 401–459. Elsevier (2001)
Yih-Ming, S., Wang, J.-F.: A novel stroke extraction method for Chinese characters using Gabor filters. Pattern Recogn. 36(3), 635–647 (2003)
Wang, W., Zhang, J., Du, J., Wang, Z.-R., Zhu, Y.: Denseran for offline handwritten Chinese character recognition. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 104–109. IEEE (2018)
Wu, C., Wang, Z.-R., Du, J., Zhang, J., Wang, J.: Joint spatial and radical analysis network for distorted Chinese character recognition. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 5, pp. 122–127. IEEE (2019)
Wu, C., Fan, W., He, Y., Sun, J., Naoi, S.: Handwritten character recognition by alternately trained relaxation convolutional neural network. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 291–296. IEEE (2014)
Xin, J., Wang, N., Jiang, X., Li, J., Gao, X., Li, Z.: Facial attribute capsules for noise face super resolution. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12476–12483 (2020)
Yin, F., Wang, Q.-F., Zhang, X.-Y., Liu, C.-L.: ICDAR 2013 Chinese handwriting recognition competition. In: 12th International Conference on Document Analysis and Recognition, pp. 1464–1470. IEEE (2013)
Yu, C., Zhu, X., Zhang, X., Wang, Z., Zhang, Z., Lei, Z.: HP-Capsule: unsupervised face part discovery by hierarchical parsing capsule network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4022–4031 (2022)
Yu, C., Zhu, X., Zhang, X., Zhang, Z., Lei, Z.: Graphics capsule: learning hierarchical 3D face representations from 2D images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20981–20990 (2023)
Zhang, X., Li, P., Jia, W., Zhao, H.: Multi-labeled relation extraction with attentive capsule network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7484–7491 (2019)
Zhang, X.-Y., Bengio, Y., Liu, C.-L.: Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recogn. 61, 348–360 (2017)
Zhang, X.-Y., Yin, F., Zhang, Y.-M., Liu, C.-L., Bengio, Y.: Drawing and recognizing Chinese characters with recurrent neural network. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 849–862 (2017)
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Wu, XJ., Ao, X., Zhang, RS., Liu, CL. (2024). Structural Recognition of Handwritten Chinese Characters Using a Modified Part Capsule Auto-encoder. In: Fang, L., Pei, J., Zhai, G., Wang, R. (eds) Artificial Intelligence. CICAI 2023. Lecture Notes in Computer Science(), vol 14473. Springer, Singapore. https://doi.org/10.1007/978-981-99-8850-1_39
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