Abstract
Images of historical Vietnamese steles allow historians to discover invaluable information regarding the past of the country, especially about the life of people in rural villages. Due to the sheer amount of available stone engravings and their diverseness, manual examination is difficult and time-consuming. Therefore, automatic document analysis methods based on machine learning could immensely facilitate this laborious work. However, creating ground truth for machine learning is also complex and time-consuming for human experts, which is why synthetic training samples greatly support learning while reducing human effort. In particular, they can be used to train deep neural networks for character detection and recognition. In this paper, we present a method for creating synthetic engravings and use it to create a new database composed of 26,901 synthetic Chu Nom characters in 21 different styles. Using a machine learning model for unpaired image-to-image translation, our approach is annotation-free, i.e. there is no need for human experts to label character images. A user study demonstrates that the synthetic engravings look realistic to the human eye.
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References
Cai, J., Peng, L., Tang, Y., Liu, C., Li, P.: TH-GAN: generative adversarial network based transfer learning for historical Chinese character recognition. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 178–183 (2019)
Cha, J., Chun, S., Lee, G., Lee, B., Kim, S., Lee, H.: Few-shot compositional font generation with dual memory. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12364, pp. 735–751. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58529-7_43
Gan, J., Wang, W.: HiGAN: handwriting imitation conditioned on arbitrary-length texts and disentangled styles. AAAI Conf. Artif. Intell. 35(9), 7484–7492 (2021)
Goodfellow, I., et al.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems (NIPS), pp. 2672–2680 (2014)
Guan, M., Ding, H., Chen, K., Huo, Q.: Improving handwritten OCR with augmented text line images synthesized from online handwriting samples by style-conditioned GAN. In: International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 151–156 (2020)
Gui, J., Sun, Z., Wen, Y., Tao, D., Ye, J.: A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Trans. Knowl. Data Eng. pp. 1–20 (2021)
Hayashi, H., Abe, K., Uchida, S.: GlyphGAN: style-consistent font generation based on generative adversarial networks. Knowl. Based Syst. 186, 1–13 (2019)
Hong, Y., Hwang, U., Yoo, J., Yoon, S.: How generative adversarial networks and their variants work. ACM Comput. Surv. 52(1), 1–43 (2019)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976 (2017)
Jiang, Y., Lian, Z., Tang, Y., Xiao, J.: SCFont: structure-guided Chinese font generation via deep stacked networks. AAAI Conf. Artif. Intell. 33(01), 4015–4022 (2019)
Kang, L., Riba, P., Wang, Y., Rusiñol, M., Fornés, A., Villegas, M.: GANwriting: content-conditioned generation of styled handwritten word images. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 273–289. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58592-1_17
Lin, X., Li, J., Zeng, H., Ji, R.: Font generation based on least squares conditional generative adversarial nets. Multimedia Tools Appl. 78(1), 783–797 (2018). https://doi.org/10.1007/s11042-017-5457-4
Liu, J., Gu, C., Wang, J., Youn, G., Kim, J.-U.: Multi-scale multi-class conditional generative adversarial network for handwritten character generation. J. Supercomput. 75(4), 1922–1940 (2017). https://doi.org/10.1007/s11227-017-2218-0
Liu, X., Meng, G., Xiang, S., Pan, C.: FontGAN: a unified generative framework for Chinese character stylization and de-stylization. CoRR abs/1910.12604 (2019)
Nishat, Z.K., Shopon, M.: Synthetic class specific Bangla handwritten character generation using conditional generative adversarial networks. In: International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–5 (2019)
van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. CoRR abs/1807.03748 (2019)
Papin, P.: Aperçu sur le programme « publication de l’inventaire et du corpus complet des inscriptions sur stèles du viêt-nam ». Bulletin de l’Ecole française d’Extrême-Orient 90(1), 465–472 (2003)
Papin, P.: Les inscriptions anciennes du viêt-nam, source d’une nouvelle vision des xviie et xviiie siêcles. Good Morning 105 (2010)
Park, T., Efros, A.A., Zhang, R., Zhu, J.-Y.: Contrastive learning for unpaired image-to-image translation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 319–345. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_19
Qin, M., Chen, X.: Restore the incomplete calligraphy based on style transfer. In: Chinese Control Conference (CCC), pp. 8812–8817 (2019)
Scius-Bertrand, A., Voegtlin, L., Alberti, M., Fischer, A., Bui, M.: Layout analysis and text column segmentation for historical Vietnamese steles. In: Proceedings of 5th International Workshop on Historical Document Imaging and Processing (HIP), pp. 84–89 (2019)
Scius-Bertrand, A., Jungo, M., Wolf, B., Fischer, A., Bui, M.: Annotation-free character detection in historical Vietnamese stele images. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 432–447 (2021)
Tian, Y.: Master Chinese calligraphy with conditional adversarial networks (2017). https://github.com/kaonashi-tyc/zi2zi
Wen, C., et al.: Handwritten Chinese font generation with collaborative stroke refinement. In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3882–3891 (2021)
Wu, L., Chen, X., Meng, L., Meng, X.: Multitask adversarial learning for Chinese font style transfer. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2020)
Wu, S.J., Yang, C.Y., Hsu, J.Y.J.: CalliGAN: style and structure-aware Chinese calligraphy character generator. CoRR abs/2005.12500 (2020)
Xi, Y., Yan, G., Hua, J., Zhong, Z.: JointFontGAN: joint geometry-content GAN for font generation via few-shot learning. ACM Int. Conf. Multimedia, pp. 4309–4317 (2020)
Zeng, J., Chen, Q., Liu, Y., Wang, M., Yao, Y.: StrokeGAN: reducing mode collapse in Chinese font generation via stroke encoding. CoRR abs/2012.08687 (2021)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: International Conference on Computer Vision (ICCV), pp. 2242–2251 (2017)
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Diesbach, J., Fischer, A., Bui, M., Scius-Bertrand, A. (2022). Generating Synthetic Styled Chu Nom Characters. In: Porwal, U., Fornés, A., Shafait, F. (eds) Frontiers in Handwriting Recognition. ICFHR 2022. Lecture Notes in Computer Science, vol 13639. Springer, Cham. https://doi.org/10.1007/978-3-031-21648-0_33
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