{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T06:10:58Z","timestamp":1758089458437,"version":"3.44.0"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032046239"},{"type":"electronic","value":"9783032046246"}],"license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04624-6_15","type":"book-chapter","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T05:33:27Z","timestamp":1758000807000},"page":"257-269","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VMF-Net: Visual-Aware Multi-representation Fusion Network for\u00a0Artifact-Free Handwritten Mathematical Expressions Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9994-3691","authenticated-orcid":false,"given":"Yiming","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2570-4544","authenticated-orcid":false,"given":"Hongxi","family":"Wei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0007-3483","authenticated-orcid":false,"given":"Heng","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6459-4755","authenticated-orcid":false,"given":"Bo","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"15_CR1","unstructured":"Betker, J., et\u00a0al.: Improving image generation with better captions. Comput. Sci. 2(3), 8 (2023). https:\/\/cdnopenai.com\/papers\/dall-e-3.pdf"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Bhunia, A.K., Khan, S., Cholakkal, H., Anwer, R.M., Khan, F.S., Shah, M.: Handwriting transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1086\u20131094 (2021)","DOI":"10.1109\/ICCV48922.2021.00112"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Bian, X., Qin, B., Xin, X., Li, J., Su, X., Wang, Y.: Handwritten mathematical expression recognition via attention aggregation based bi-directional mutual learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 113\u2013121 (2022)","DOI":"10.1609\/aaai.v36i1.19885"},{"key":"15_CR4","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/978-3-031-72338-4_25","volume-title":"ICANN 2024","author":"M Chen","year":"2024","unstructured":"Chen, M., Liu, H., Dong, L.: P2H-GAN: an effective method for generating handwritten expressions. In: Wand, M., Malinovsk\u00e1, K., Schmidhuber, J., Tetko, I.V. (eds.) ICANN 2024. LNCS, vol. 15018, pp. 361\u2013376. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72338-4_25"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1511\u20131520 (2017)","DOI":"10.1109\/ICCV.2017.168"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Gao, F., Zhang, Y., Qiao, M., Wang, N.: Generating handwritten mathematical expressions from symbol graphs: an end-to-end pipeline. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15675\u201315685 (2024)","DOI":"10.1109\/CVPR52733.2024.01484"},{"key":"15_CR7","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1007\/978-3-031-73636-0_24","volume-title":"ECCV 2024","author":"G Dai","year":"2025","unstructured":"Dai, G., Zhang, Y., Ke, Q., Guo, Q., Huang, S.: One-DM: one-shot diffusion mimicker for handwritten text generation. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) ECCV 2024. LNCS, vol. 15116, pp. 410\u2013427. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-73636-0_24"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Fu, Y., Cai, W., Gao, M., Zhou, A.: Symbol location-aware network for improving handwritten mathematical expression recognition. In: Proceedings of the 2023 ACM International Conference on Multimedia Retrieval, pp. 516\u2013524 (2023)","DOI":"10.1145\/3591106.3592259"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Gan, J., Wang, W.: HiGAN: handwriting imitation conditioned on arbitrary-length texts and disentangled styles. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 7484\u20137492 (2021)","DOI":"10.1609\/aaai.v35i9.16917"},{"key":"15_CR10","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014)"},{"key":"15_CR11","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1007\/978-3-031-72670-5_8","volume-title":"ECCV 2024","author":"T Guan","year":"2025","unstructured":"Guan, T., Lin, C., Shen, W., Yang, X.: PosFormer: recognizing complex handwritten mathematical expression with position forest transformer. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) ECCV 2024. LNCS, vol. 15080, pp. 130\u2013147. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-72670-5_8"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"15_CR13","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Johnson, J., Gupta, A., Fei-Fei, L.: Image generation from scene graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1219\u20131228 (2018)","DOI":"10.1109\/CVPR.2018.00133"},{"key":"15_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-3-030-58592-1_17","volume-title":"Computer Vision \u2013 ECCV 2020","author":"L Kang","year":"2020","unstructured":"Kang, L., Riba, P., Wang, Y., Rusi\u00f1ol, M., Forn\u00e9s, 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\u2013289. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_17"},{"key":"15_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"15_CR17","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/978-3-031-19815-1_12","volume-title":"ECCV 2022","author":"B Li","year":"2022","unstructured":"Li, B., et al.: When counting meets HMER: counting-aware network for handwritten mathematical expression recognition. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13688, pp. 197\u2013214. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19815-1_12"},{"key":"15_CR18","unstructured":"Lim, J.H., Ye, J.C.: Geometric GAN. arXiv preprint arXiv:1705.02894 (2017)"},{"key":"15_CR19","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-3-031-72998-0_16","volume-title":"ECCV 2024","author":"C Liu","year":"2025","unstructured":"Liu, C., et al.: NAMER: non-autoregressive modeling for handwritten mathematical expression recognition. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) ECCV 2024. LNCS, vol. 15115, pp. 273\u2013291. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-72998-0_16"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Mahdavi, M., Zanibbi, R., Mouchere, H., Viard-Gaudin, C., Garain, U.: ICDAR 2019 CROHME+ TFD: competition on recognition of handwritten mathematical expressions and typeset formula detection. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1533\u20131538. IEEE (2019)","DOI":"10.1109\/ICDAR.2019.00247"},{"key":"15_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1007\/978-3-031-41679-8_22","volume-title":"ICDAR 2023","author":"K Nikolaidou","year":"2023","unstructured":"Nikolaidou, K., et al.: WordStylist: styled verbatim handwritten text generation with latent diffusion models. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds.) ICDAR 2023. LNCS, vol. 14188, pp. 384\u2013401. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-41679-8_22"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Nikolaidou, K., Retsinas, G., Sfikas, G., Liwicki, M.: DiffusionPen: towards controlling the style of handwritten text generation. arXiv preprint arXiv:2409.06065 (2024)","DOI":"10.1007\/978-3-031-73013-9_24"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Pippi, V., Cascianelli, S., Cucchiara, R.: Handwritten text generation from visual archetypes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22458\u201322467 (2023)","DOI":"10.1109\/CVPR52729.2023.02151"},{"key":"15_CR24","unstructured":"Pippi, V., Quattrini, F., Cascianelli, S., Cucchiara, R.: HWD: a novel evaluation score for styled handwritten text generation. arXiv preprint arXiv:2310.20316 (2023)"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Springstein, M., M\u00fcller-Budack, E., Ewerth, R.: Unsupervised training data generation of handwritten formulas using generative adversarial networks with self-attention. In: Proceedings of the 2021 Workshop on Multi-Modal Pre-Training for Multimedia Understanding, pp. 46\u201354 (2021)","DOI":"10.1145\/3463945.3469059"},{"key":"15_CR27","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.patrec.2021.12.002","volume":"153","author":"TN Truong","year":"2022","unstructured":"Truong, T.N., Nguyen, C.T., Nakagawa, M.: Syntactic data generation for handwritten mathematical expression recognition. Pattern Recogn. Lett. 153, 83\u201391 (2022)","journal-title":"Pattern Recogn. Lett."},{"key":"15_CR28","unstructured":"Vaswani, A.: Attention is all you need. Adv. Neural Inf. Process. Syst. (2017)"},{"issue":"4","key":"15_CR29","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Wu, C., et al.: TDV2: a novel tree-structured decoder for offline mathematical expression recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 2694\u20132702 (2022)","DOI":"10.1609\/aaai.v36i3.20172"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Wu, J.W., Yin, F., Zhang, Y.M., Zhang, X.Y., Liu, C.L.: Graph-to-graph: towards accurate and interpretable online handwritten mathematical expression recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 2925\u20132933 (2021)","DOI":"10.1609\/aaai.v35i4.16399"},{"key":"15_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108910","volume":"132","author":"C Yang","year":"2022","unstructured":"Yang, C., Du, J., Zhang, J., Wu, C., Chen, M., Wu, J.: Tree-based data augmentation and mutual learning for offline handwritten mathematical expression recognition. Pattern Recogn. 132, 108910 (2022)","journal-title":"Pattern Recogn."},{"key":"15_CR33","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s10032-011-0174-4","volume":"15","author":"R Zanibbi","year":"2012","unstructured":"Zanibbi, R., Blostein, D.: Recognition and retrieval of mathematical expressions. Int. J. Doc. Anal. Recogn. (IJDAR) 15, 331\u2013357 (2012)","journal-title":"Int. J. Doc. Anal. Recogn. (IJDAR)"},{"key":"15_CR34","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/978-3-031-19815-1_23","volume-title":"ECCV 2022","author":"W Zhao","year":"2022","unstructured":"Zhao, W., Gao, L.: CoMER: modeling coverage for transformer-based handwritten mathematical expression recognition. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13688, pp. 392\u2013408. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19815-1_23"},{"key":"15_CR35","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-031-70549-6_2","volume-title":"ICDAR 2024","author":"J Zhu","year":"2024","unstructured":"Zhu, J., Gao, L., Zhao, W.: ICAL: implicit character-aided learning for enhanced handwritten mathematical expression recognition. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds.) ICDAR 2024. LNCS, vol. 14808, pp. 21\u201337. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-70549-6_2"},{"key":"15_CR36","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"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition \u2013 ICDAR 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04624-6_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T05:33:40Z","timestamp":1758000820000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04624-6_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"ISBN":["9783032046239","9783032046246"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04624-6_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,17]]},"assertion":[{"value":"17 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icdar2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}