{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T00:47:54Z","timestamp":1773449274087,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T00:00:00Z","timestamp":1674604800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T00:00:00Z","timestamp":1674604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s00530-023-01047-4","type":"journal-article","created":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T15:02:26Z","timestamp":1674658946000},"page":"1291-1300","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Style transfer network for complex multi-stroke text"],"prefix":"10.1007","volume":"29","author":[{"given":"Fangmei","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fasheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuming","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"key":"1047_CR1","doi-asserted-by":"crossref","unstructured":"Yang, S., Liu, J., Lian, Z., Guo, Z.: Awesome typography: statistics-based text effects transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137473 (2017)","DOI":"10.1109\/CVPR.2017.308"},{"key":"1047_CR2","doi-asserted-by":"crossref","unstructured":"Yang, S., Liu, J., Wang, W., Guo, Z.: Tet-gan: text effects transfer via stylization and destylization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1238\u20131245 (2019)","DOI":"10.1609\/aaai.v33i01.33011238"},{"issue":"10","key":"1047_CR3","doi-asserted-by":"publisher","first-page":"3709","DOI":"10.1109\/TPAMI.2020.2983697","volume":"43","author":"S Yang","year":"2020","unstructured":"Yang, S., Wang, W., Liu, J.: Te141k: artistic text benchmark for text effect transfer. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3709\u20133723 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"1047_CR4","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1007\/s11063-019-10041-9","volume":"52","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Han, T., Gao, Z.: Pairwise generalization network for cross-domain image recognition. Neural Process. Lett. 52(2), 1023\u20131041 (2020)","journal-title":"Neural Process. Lett."},{"key":"1047_CR5","doi-asserted-by":"crossref","unstructured":"Yang, S., Wang, Z., Wang, Z., Xu, N., Liu, J., Guo, Z.: Controllable artistic text style transfer via shape-matching gan. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4442\u20134451 (2019)","DOI":"10.1109\/ICCV.2019.00454"},{"issue":"8","key":"1047_CR6","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.23940\/ijpe.20.08.p14.12711278","volume":"16","author":"F Zhang","year":"2020","unstructured":"Zhang, F., Yang, Y., Huang, W., Zhang, G., Wang, J.: Improving font effect generation based on pyramid style feature. Int. J. Perform. Eng. 16(8), 1271\u20131278 (2020)","journal-title":"Int. J. Perform. Eng."},{"key":"1047_CR7","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)"},{"key":"1047_CR8","first-page":"18795","volume":"33","author":"J Zhuang","year":"2020","unstructured":"Zhuang, J., Tang, T., Ding, Y., Tatikonda, S.C., Dvornek, N., Papademetris, X., Duncan, J.: Adabelief optimizer: adapting stepsizes by the belief in observed gradients. Adv. Neural Inf. Process. Syst. 33, 18795\u201318806 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1047_CR9","first-page":"139","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 139\u2013144 (2014)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1047_CR10","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"1047_CR11","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"1047_CR12","unstructured":"Kim, J., Kim, M., Kang, H., Lee, K.: U-gat-it: unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. arXiv preprint arXiv:1907.10830 (2019)"},{"key":"1047_CR13","doi-asserted-by":"crossref","unstructured":"Oeldorf, C., Spanakis, G.: Loganv2: conditional style-based logo generation with generative adversarial networks. In: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pp. 462\u2013468. IEEE (2019)","DOI":"10.1109\/ICMLA.2019.00086"},{"issue":"3","key":"1047_CR14","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1007\/s11063-019-10024-w","volume":"50","author":"L Zhan","year":"2019","unstructured":"Zhan, L., Wang, Y.: Stable and refined style transfer using zigzag learning algorithm. Neural Process. Lett. 50(3), 2481\u20132492 (2019)","journal-title":"Neural Process. Lett."},{"key":"1047_CR15","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015)","DOI":"10.1167\/16.12.326"},{"key":"1047_CR16","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1047_CR17","doi-asserted-by":"crossref","unstructured":"Li, C., Wand, M.: Combining markov random fields and convolutional neural networks for image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2479\u20132486 (2016)","DOI":"10.1109\/CVPR.2016.272"},{"key":"1047_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02178-3","author":"M Shajini","year":"2021","unstructured":"Shajini, M., Ramanan, A.: A knowledge-sharing semi-supervised approach for fashion clothes classification and attribute prediction. Vis. Comput. (2021). https:\/\/doi.org\/10.1007\/s00371-021-02178-3","journal-title":"Vis. Comput."},{"issue":"2","key":"1047_CR19","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/s00371-018-1609-4","volume":"36","author":"L Wang","year":"2020","unstructured":"Wang, L., Wang, Z., Yang, X., Hu, S.-M., Zhang, J.: Photographic style transfer. Vis. Comput. 36(2), 317\u2013331 (2020). https:\/\/doi.org\/10.1007\/s00371-018-1609-4","journal-title":"Vis. Comput."},{"issue":"4","key":"1047_CR20","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1007\/s00371-019-01661-2","volume":"36","author":"G Lian","year":"2020","unstructured":"Lian, G., Zhang, K.: Transformation of portraits to picasso\u2019s cubism style. Vis. Comp. 36(4), 799\u2013807 (2020)","journal-title":"Vis. Comp."},{"issue":"12","key":"1047_CR21","doi-asserted-by":"publisher","first-page":"1725","DOI":"10.1007\/s00371-017-1452-z","volume":"34","author":"A Aristidou","year":"2018","unstructured":"Aristidou, A., Stavrakis, E., Papaefthimiou, M., Papagiannakis, G., Chrysanthou, Y.: Style-based motion analysis for dance composition. Vis. Comput. 34(12), 1725\u20131737 (2018)","journal-title":"Vis. Comput."},{"key":"1047_CR22","doi-asserted-by":"crossref","unstructured":"Azadi, S., Fisher, M., Kim, V.G., Wang, Z., Shechtman, E., Darrell, T.: Multi-content gan for few-shot font style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7564\u20137573 (2018)","DOI":"10.1109\/CVPR.2018.00789"},{"key":"1047_CR23","doi-asserted-by":"crossref","unstructured":"Li, C., Taniguchi, Y., Lu, M., Konomi, S.: Few-shot font style transfer between different languages. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 433\u2013442 (2021)","DOI":"10.1109\/WACV48630.2021.00048"},{"key":"1047_CR24","doi-asserted-by":"crossref","unstructured":"Wen, Q., Li, S., Han, B., Yuan, Y.: Zigan: fine-grained Chinese calligraphy font generation via a few-shot style transfer approach. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 621\u2013629 (2021)","DOI":"10.1145\/3474085.3475225"},{"key":"1047_CR25","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Ito, Y., Nakano, K.: Art font image generation with conditional generative adversarial networks. In: 2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW), pp. 151\u2013156 (2020). IEEE","DOI":"10.1109\/CANDARW51189.2020.00039"},{"key":"1047_CR26","doi-asserted-by":"crossref","unstructured":"Yuan, H., Yanai, K.: Multi-style transfer generative adversarial network for text images. In: 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 63\u201369 (2021). IEEE","DOI":"10.1109\/MIPR51284.2021.00017"},{"key":"1047_CR27","doi-asserted-by":"crossref","unstructured":"Atarsaikhan, G., Iwana, B.K., Uchida, S.: Neural style difference transfer and its application to font generation. In: International Workshop on Document Analysis Systems, pp. 544\u2013558 (2020). Springer","DOI":"10.1007\/978-3-030-57058-3_38"},{"key":"1047_CR28","doi-asserted-by":"crossref","unstructured":"Wang, W., Liu, J., Yang, S., Guo, Z.: Typography with decor: intelligent text style transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5889\u20135897 (2019)","DOI":"10.1109\/CVPR.2019.00604"},{"key":"1047_CR29","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. ArXiv abs\/1802.05957 (2018)"},{"key":"1047_CR30","unstructured":"Odena, A., Buckman, J., Olsson, C., Brown, T., Olah, C., Raffel, C., Goodfellow, I.: Is generator conditioning causally related to gan performance? In: International Conference on Machine Learning, pp. 3849\u20133858 (2018). PMLR"},{"key":"1047_CR31","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: International Conference on Machine Learning, pp. 7354\u20137363. PMLR (2019)"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-023-01047-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-023-01047-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-023-01047-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T10:16:23Z","timestamp":1685441783000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-023-01047-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,25]]},"references-count":31,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["1047"],"URL":"https:\/\/doi.org\/10.1007\/s00530-023-01047-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2216164\/v1","asserted-by":"object"}]},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,25]]},"assertion":[{"value":"29 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}