{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T04:20:47Z","timestamp":1782879647891,"version":"3.54.5"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819543946","type":"print"},{"value":"9789819543953","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"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\/978-981-95-4395-3_8","type":"book-chapter","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T10:29:44Z","timestamp":1762770584000},"page":"100-114","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Few-Shot Connectivity-Aware Text Line Segmentation in\u00a0Historical Documents"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0029-8463","authenticated-orcid":false,"given":"Rafael","family":"Sterzinger","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9825-686X","authenticated-orcid":false,"given":"Tingyu","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4195-1593","authenticated-orcid":false,"given":"Robert","family":"Sablatnig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Alberti, M., V\u00f6gtlin, L., Pondenkandath, V., Seuret, M., Ingold, R., Liwicki, M.: Labeling, cutting, grouping: an efficient text line segmentation method for medieval manuscripts. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1200\u20131206. IEEE (2019)","DOI":"10.1109\/ICDAR.2019.00194"},{"key":"8_CR2","unstructured":"AVML Lab Fest Organizers: FEST @ ICDAR 2025: Competition on few-shot text line segmentation of ancient handwritten documents (2025)"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Barakat, B., Droby, A., Kassis, M., El-Sana, J.: Text line segmentation for challenging handwritten document images using fully convolutional network. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 374\u2013379. IEEE (2018)","DOI":"10.1109\/ICFHR-2018.2018.00072"},{"issue":"4","key":"8_CR4","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/tpami.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2018). https:\/\/doi.org\/10.1109\/tpami.2017.2699184","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"De\u00a0Nardin, A., Zottin, S., Colombi, E., Piciarelli, C., Foresti, G.L.: Is ImageNet always the best option? An overview on transfer learning strategies for document layout analysis. In: International Conference on Image Analysis and Processing, pp. 489\u2013499. Springer (2023)","DOI":"10.1007\/978-3-031-51026-7_41"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"De\u00a0Nardin, A., Zottin, S., Paier, M., Foresti, G.L., Colombi, E., Piciarelli, C.: Efficient few-shot learning for pixel-precise handwritten document layout analysis. In: 2023 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE (2023)","DOI":"10.1109\/WACV56688.2023.00367"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"De\u00a0Nardin, A., Zottin, S., Piciarelli, C., Colombi, E., Foresti, G.L.: Few-shot pixel-precise document layout segmentation via dynamic instance generation and local thresholding. Int. J. Neural Syst. 33(10) (2023)","DOI":"10.1142\/S0129065723500521"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"De\u00a0Nardin, A., Zottin, S., Piciarelli, C., Colombi, E., Foresti, G.L.: A one-shot learning approach to document layout segmentation of ancient Arabic manuscripts. In: 2024 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE (2024)","DOI":"10.1109\/WACV57701.2024.00794"},{"key":"8_CR9","doi-asserted-by":"publisher","unstructured":"Diem, M., Kleber, F., Fiel, S., Gruning, T., Gatos, B.: cBAD: ICDAR2017 competition on baseline detection. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 1355\u20131360. IEEE (2017). https:\/\/doi.org\/10.1109\/icdar.2017.222","DOI":"10.1109\/icdar.2017.222"},{"issue":"3","key":"8_CR10","doi-asserted-by":"publisher","first-page":"65","DOI":"10.3390\/jimaging10030065","volume":"10","author":"FC Fizaine","year":"2024","unstructured":"Fizaine, F.C., et al.: Historical text line segmentation using deep learning algorithms: Mask-RCNN against U-Net networks. J. Imag. 10(3), 65 (2024)","journal-title":"J. Imag."},{"key":"8_CR11","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Grim, A., Chandrashekar, J., S\u00fcmb\u00fcl, U.: Efficient connectivity-preserving instance segmentation with super voxel-based loss function. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 3, pp. 3167\u20133175 (2025). https:\/\/doi.org\/10.1609\/aaai.v39i3.32326","DOI":"10.1609\/aaai.v39i3.32326"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Likforman-Sulem, L., Hanimyan, A., Faure, C.: A Hough-based algorithm for extracting text lines in handwritten documents. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol.\u00a02, pp. 774\u2013777. IEEE (1995)","DOI":"10.1109\/ICDAR.1995.602017"},{"key":"8_CR15","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s10032-006-0023-z","volume":"9","author":"L Likforman-Sulem","year":"2007","unstructured":"Likforman-Sulem, L., Zahour, A., Taconet, B.: Text line segmentation of historical documents: a survey. IJDAR 9, 123\u2013138 (2007)","journal-title":"IJDAR"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Mosinska, A., Marquez-Neila, P., Kozi\u0144ski, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136\u20133145 (2018)","DOI":"10.1109\/CVPR.2018.00331"},{"issue":"11","key":"8_CR18","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.1109\/34.244677","volume":"15","author":"L O\u2019Gorman","year":"1993","unstructured":"O\u2019Gorman, L.: The document spectrum for page layout analysis. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1162\u20131173 (1993)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: International Workshop on Machine Learning in Medical Imaging, pp. 379\u2013387. Springer (2017)","DOI":"10.1007\/978-3-319-67389-9_44"},{"issue":"1","key":"8_CR21","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/0167-8655(93)90134-Y","volume":"14","author":"V Shapiro","year":"1993","unstructured":"Shapiro, V., Gluhchev, G., Sgurev, V.: Handwritten document image segmentation and analysis. Pattern Recogn. Lett. 14(1), 71\u201378 (1993)","journal-title":"Pattern Recogn. Lett."},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Simistira, F., et al.: ICDAR2017 competition on layout analysis for challenging medieval manuscripts. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol.\u00a001, pp. 1361\u20131370 (2017). https:\/\/doi.org\/10.1109\/ICDAR.2017.223","DOI":"10.1109\/ICDAR.2017.223"},{"key":"8_CR23","doi-asserted-by":"publisher","unstructured":"Sterzinger, R., Brenner, S., Sablatnig, R.: Drawing the line: deep segmentation for extracting art from ancient Etruscan mirrors. In: ICDAR 2024, pp. 39\u201356. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-70543-4_3","DOI":"10.1007\/978-3-031-70543-4_3"},{"key":"8_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-67558-9_28","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"CH Sudre","year":"2017","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_28"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Vadlamudi, N., Krishna, R., Sarvadevabhatla, R.K.: SeamFormer: high precision text line segmentation for handwritten documents. In: International Conference on Document Analysis and Recognition, pp. 313\u2013331. Springer (2023)","DOI":"10.1007\/978-3-031-41685-9_20"},{"key":"8_CR26","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol.\u00a034, pp. 12077\u201312090. Curran Associates, Inc. (2021)"},{"key":"8_CR27","doi-asserted-by":"publisher","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017). https:\/\/doi.org\/10.1109\/cvpr.2017.660","DOI":"10.1109\/cvpr.2017.660"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3\u201311. Springer (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"issue":"20","key":"8_CR29","doi-asserted-by":"publisher","first-page":"11777","DOI":"10.1007\/s00521-023-09356-5","volume":"36","author":"S Zottin","year":"2024","unstructured":"Zottin, S., De Nardin, A., Colombi, E., Piciarelli, C., Pavan, F., Foresti, G.L.: U-DIADS-Bib: a full and few-shot pixel-precise dataset for document layout analysis of ancient manuscripts. Neural Comput. Appl. 36(20), 11777\u201311789 (2024). https:\/\/doi.org\/10.1007\/s00521-023-09356-5","journal-title":"Neural Comput. Appl."},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Zottin, S., De\u00a0Nardin, A., Foresti, G.L., Colombi, E., Piciarelli, C.: ICDAR 2024 competition on few-shot and many-shot layout segmentation of ancient manuscripts (SAM). In: International Conference on Document Analysis and Recognition, pp. 315\u2013331. Springer (2024)","DOI":"10.1007\/978-3-031-70552-6_19"},{"key":"8_CR31","unstructured":"Zottin, S., et al.: Exploring few-shot text line segmentation approaches in challenging ancient manuscripts. In: Cornia, M., et al. (eds.) Proceedings of the 21st Conference on Information and Research science Connecting to Digital and Library Science, Udine, Italy, February 20\u201321, 2025. CEUR Workshop Proceedings, vol.\u00a03937. CEUR-WS.org (2025)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4395-3_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T10:29:49Z","timestamp":1762770589000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4395-3_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,11]]},"ISBN":["9789819543946","9789819543953"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4395-3_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,11]]},"assertion":[{"value":"11 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gold Coast, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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":"10 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.acpr2025.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}