{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:59:12Z","timestamp":1778083152899,"version":"3.51.4"},"reference-count":101,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:00:00Z","timestamp":1746576000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:00:00Z","timestamp":1746576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Sami Shamoon College of Engineering"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["IJDAR"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The purpose of this survey is to provide a comprehensive overview of recent advancements in text line segmentation and baseline detection techniques within the analysis of historical document images. Text line extraction is an essential step in the historical documents image analysis pipeline, as its results significantly impact the accuracy of subsequent processes, such as handwritten text recognition (HTR). Through a multi-stage procedure, we carefully selected 49 peer-reviewed studies published since 2019. Based on careful analysis of these studies, we summarize the information of the existing datasets, describe and categorize different methods, and summarize evaluation protocols. In addition, we compare the results of various methods on benchmark datasets. Finally, we highlight the gaps and suggest directions for future research. We believe that this comprehensive survey will be of great assistance to researchers working in the field of historical document image analysis, as it offers critical insights into the latest advancements and developments, providing a foundation for future research.<\/jats:p>","DOI":"10.1007\/s10032-025-00526-w","type":"journal-article","created":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T11:59:35Z","timestamp":1746619175000},"page":"3-39","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Recent advances in text line segmentation and baseline detection in historical document images: a systematic review"],"prefix":"10.1007","volume":"29","author":[{"given":"Irina","family":"Rabaev","sequence":"first","affiliation":[]},{"given":"Marina","family":"Litvak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,7]]},"reference":[{"key":"526_CR1","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/978-3-031-78495-8_14","volume-title":"Pattern Recognition","author":"V Agrawal","year":"2025","unstructured":"Agrawal, V., Vadlamudi, N., Waseem, M., Joseph, A., Chitluri, S., Sarvadevabhatla, R.K.: LineTR: Unified text line segmentation for challenging palm leaf manuscripts. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, C.-L., Bhattacharya, S., Pal, U. (eds.) Pattern Recognition, pp. 217\u2013233. Springer, Cham (2025)"},{"key":"526_CR2","doi-asserted-by":"publisher","unstructured":"Alberti, M., Bouillon, M., Ingold, R., Liwicki, M.: Open evaluation tool for layout analysis of document images. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 04, pp. 43\u201347 (2017). https:\/\/doi.org\/10.1109\/ICDAR.2017.311","DOI":"10.1109\/ICDAR.2017.311"},{"key":"526_CR3","doi-asserted-by":"crossref","unstructured":"Alberti, M., Vogtlin, 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). IEEE, pp. 1200\u20131206 (2019)","DOI":"10.1109\/ICDAR.2019.00194"},{"key":"526_CR4","doi-asserted-by":"publisher","unstructured":"Azran, A., Schclar, A., Saabni, R.: Text line extraction using deep learning and minimal sub seams. In: ACM, pp. 1\u20134 (2021). ISBN 9781450385961. https:\/\/doi.org\/10.1145\/3469096.3474941","DOI":"10.1145\/3469096.3474941"},{"key":"526_CR5","doi-asserted-by":"publisher","unstructured":"Barakat, B.K., Cohen, R., Rabaev, I., El-Sana, J.: Vml-moc: Segmenting a multiply oriented and curved handwritten text line dataset, vol. 9. IEEE, pp. 13\u201318 (2019). ISBN 978-1-7281-5054-3. https:\/\/doi.org\/10.1109\/ICDARW.2019.50109","DOI":"10.1109\/ICDARW.2019.50109"},{"issue":"8276","key":"526_CR6","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/app10228276","volume":"10","author":"BK Barakat","year":"2020","unstructured":"Barakat, B.K., Cohen, R., Droby, A., Rabaev, I., El-Sana, J.: Learning-free text line segmentation for historical handwritten documents. Appl. Sci. 10(8276), 11 (2020). https:\/\/doi.org\/10.3390\/app10228276","journal-title":"Appl. Sci."},{"key":"526_CR7","doi-asserted-by":"publisher","unstructured":"Barakat, Berat\u00a0Kurar., Droby, Ahmad., Alaasam, Reem., Madi, Boraq., Rabaev, Irina., Shammes, Raed., El-Sana, Jihad.: Unsupervised deep learning for text line segmentation. IEEE, 1, pp. 2304\u20132311 (2021). ISBN 978-1-7281-8808-9. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9413308","DOI":"10.1109\/ICPR48806.2021.9413308"},{"key":"526_CR8","unstructured":"Bernard, G., Wall, C., Boillet, M., Coustaty, M., Kermorvant, C., Doucet, A.: Pares: Parish registry survey - historical census table dataset (19th, 20th centuries) - France (2023)"},{"key":"526_CR9","doi-asserted-by":"crossref","unstructured":"Bernard, Guillaume., Wall, Casey., Boillet, M\u00e9lodie., Coustaty, Micka\u00ebl., Kermorvant, Christopher., Doucet, Antoine.: Text line detection in historical index tables: Evaluations on a new french parish record survey dataset (pares). In: International Conference on Asian Digital Libraries. Springer, pp. 59\u201375 (2023)","DOI":"10.1007\/978-981-99-8085-7_6"},{"key":"526_CR10","doi-asserted-by":"publisher","unstructured":"Biswas, C., Mukherjee, P.S., Ghosh, K., Bhattacharya, U., Parui, S.K.: A hybrid deep architecture for robust recognition of text lines of degraded printed documents. IEEE, 8, pp. 3174\u20133179 (2018). ISBN 978-1-5386-3788-3. https:\/\/doi.org\/10.1109\/ICPR.2018.8545409","DOI":"10.1109\/ICPR.2018.8545409"},{"key":"526_CR11","doi-asserted-by":"publisher","unstructured":"Boillet, M\u00e9lodie., Bonhomme, Marie-Laurence., Stutzmann, Dominique., Kermorvant, Christopher.: Horae: An annotated dataset of books of hours. In: Proceedings of the 5th International Workshop on Historical Document Imaging and Processing, HIP\u201919, New York, NY, USA. Association for Computing Machinery, pp. 7\u201312 (2019). ISBN 9781450376686. https:\/\/doi.org\/10.1145\/3352631.3352633","DOI":"10.1145\/3352631.3352633"},{"key":"526_CR12","doi-asserted-by":"publisher","first-page":"2134","DOI":"10.1109\/ICPR48806.2021.9412447","volume":"1","author":"M Boillet","year":"2021","unstructured":"Boillet, M., Kermorvant, C., Paquet, T.: Multiple document datasets pre-training improves text line detection with deep neural networks. IEEE 1, 2134\u20132141 (2021). https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412447","journal-title":"IEEE"},{"key":"526_CR13","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s10032-022-00395-7","volume":"25","author":"M Boillet","year":"2022","unstructured":"Boillet, M., Kermorvant, C., Paquet, T.: Robust text line detection in historical documents: learning and evaluation methods. Int. J. Doc. Anal. Recognit. 25, 95\u2013114 (2022). https:\/\/doi.org\/10.1007\/s10032-022-00395-7","journal-title":"Int. J. Doc. Anal. Recognit."},{"key":"526_CR14","doi-asserted-by":"publisher","unstructured":"Boro\u015f, E., Romero, V., Maarand, M., Zenklov\u00e1, K., K\u0159e\u010dkov\u00e1, J., Vidal, E., Stutzmann, D., Kermorvant, C.: A comparison of sequential and combined approaches for named entity recognition in a corpus of handwritten medieval charters. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 79\u201384, (2020). https:\/\/doi.org\/10.1109\/ICFHR2020.2020.00025","DOI":"10.1109\/ICFHR2020.2020.00025"},{"key":"526_CR15","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1007\/978-3-031-21648-0_30","volume-title":"Frontiers in Handwriting Recognition","author":"H Cheng","year":"2022","unstructured":"Cheng, H., Jian, C., Wu, S., Jin, L.: Scut-cab: a new benchmark dataset of ancient Chinese books with complex layouts for document layout analysis. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition, pp. 436\u2013451. Springer, Cham (2022)"},{"key":"526_CR16","doi-asserted-by":"publisher","unstructured":"Clausner, C., Antonacopoulos, A., Mcgregor, N., Wilson-Nunn, D.: Icfhr 2018 competition on recognition of historical Arabic scientific manuscripts - rasm2018. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 471\u2013476 (2018). https:\/\/doi.org\/10.1109\/ICFHR-2018.2018.00088","DOI":"10.1109\/ICFHR-2018.2018.00088"},{"key":"526_CR17","unstructured":"Dhali, M.A., de\u00a0Wit, J.W., Schomaker, L.: Binet: Degraded-manuscript binarization in diverse document textures and layouts using deep encoder-decoder networks. arXiv preprint arXiv:1911.07930 (2019)"},{"key":"526_CR18","doi-asserted-by":"publisher","unstructured":"Diem, M., Kleber, F., Fiel, S., Gr\u00fcning, T., Gatos, B.: cbad: Icdar2017 competition on baseline detection. In: 2017 14th IAPR International Conference on Document Analysis and Recognition , 01, pp. 1355\u20131360 (2017). https:\/\/doi.org\/10.1109\/ICDAR.2017.222","DOI":"10.1109\/ICDAR.2017.222"},{"key":"526_CR19","doi-asserted-by":"publisher","first-page":"1494","DOI":"10.1109\/ICDAR.2019.00240","volume":"9","author":"M Diem","year":"2019","unstructured":"Diem, M., Kleber, F., Sablatnig, R., Gatos, B.: cbad: Icdar 2019 competition on baseline detection. IEEE 9, 1494\u20131498 (2019). https:\/\/doi.org\/10.1109\/ICDAR.2019.00240","journal-title":"IEEE"},{"key":"526_CR20","unstructured":"DIVA-DIA. DIVA_Line_Segmentation_Evaluator. https:\/\/github.com\/DIVA-DIA\/DIVA_Line_Segmentation_Evaluator, Year of last update, or access date. Accessed 30 Nov 2023"},{"issue":"1","key":"526_CR21","doi-asserted-by":"publisher","first-page":"14","DOI":"10.53508\/ijiam.1407236","volume":"7","author":"S Djaghbellou","year":"2024","unstructured":"Djaghbellou, S., Att\u0131a, A., Bouz\u0131ane, A.: A survey on text-line segmentation in Arab historical manuscripts. Int. J. Inform. Appl. Math. 7(1), 14\u201332 (2024). https:\/\/doi.org\/10.53508\/ijiam.1407236","journal-title":"Int. J. Inform. Appl. Math."},{"key":"526_CR22","doi-asserted-by":"publisher","unstructured":"Dolfing, H.J.G.A., Bellegarda, J., Chorowski, J., Marxer, R., Laurent, A.: The \u201cscribblelens\u201d dutch historical handwriting corpus. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 67\u201372 (2020). https:\/\/doi.org\/10.1109\/ICFHR2020.2020.00023","DOI":"10.1109\/ICFHR2020.2020.00023"},{"issue":"9","key":"526_CR23","doi-asserted-by":"publisher","first-page":"9528","DOI":"10.3390\/app12199528","volume":"12","author":"A Droby","year":"2022","unstructured":"Droby, A., Barakat, B.K., Saabni, R., Alaasam, R., Madi, B., El-Sana, J.: Understanding unsupervised deep learning for text line segmentation. Appl. Sci. 12(9), 9528 (2022a). https:\/\/doi.org\/10.3390\/app12199528","journal-title":"Appl. Sci."},{"issue":"3","key":"526_CR24","doi-asserted-by":"publisher","first-page":"535","DOI":"10.3390\/signals3030032","volume":"3","author":"A Droby","year":"2022","unstructured":"Droby, A., Barakat, B.K., Alaasam, R., Madi, B., Rabaev, I., El-Sana, J.: Text line extraction in historical documents using mask r-cnn. Signals 3(3), 535\u2013549 (2022b). https:\/\/doi.org\/10.3390\/signals3030032","journal-title":"Signals"},{"key":"526_CR25","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/s10032-021-00370-8","volume":"24","author":"A Dutta","year":"2021","unstructured":"Dutta, A., Garai, A., Biswas, S., Das, A.K.: Segmentation of text lines using multi-scale cnn from warped printed and handwritten document images. Int. J. Doc. Anal. Recognit. 24, 299\u2013313 (2021). https:\/\/doi.org\/10.1007\/s10032-021-00370-8","journal-title":"Int. J. Doc. Anal. Recognit."},{"key":"526_CR26","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ijleo.2019.04.128","volume":"188","author":"MH Mohamed Dyla","year":"2019","unstructured":"Mohamed Dyla, M.H., Morain-Nicolier, F.: Text line segmentation and binarization of handwritten historical documents using the fast and adaptive bidimensional empirical mode decomposition. Optik 188, 52\u201363 (2019). https:\/\/doi.org\/10.1016\/j.ijleo.2019.04.128","journal-title":"Optik"},{"key":"526_CR27","first-page":"1","volume":"240","author":"M Ester","year":"1996","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: Density-based spatial clustering of applications with noise. Int. Conf. Knowl. Discov. Data Min. 240, 1 (1996)","journal-title":"Int. Conf. Knowl. Discov. Data Min."},{"key":"526_CR28","doi-asserted-by":"publisher","unstructured":"Fischer, A., Inderm\u00fchle, E., Bunke, H., Viehhauser, G., Stolz, M.: Ground truth creation for handwriting recognition in historical documents. In: 9th IAPR International Workshop on Document Analysis Systems, pp. 3\u201310 (2010). https:\/\/doi.org\/10.1145\/1815330.1815331","DOI":"10.1145\/1815330.1815331"},{"issue":"3","key":"526_CR29","doi-asserted-by":"publisher","first-page":"136","DOI":"10.3390\/a16030136","volume":"16","author":"N Fischer","year":"2023","unstructured":"Fischer, N., Hartelt, A., Puppe, F.: Line-level layout recognition of historical documents with background knowledge. Algorithms 16(3), 136 (2023)","journal-title":"Algorithms"},{"issue":"3","key":"526_CR30","doi-asserted-by":"publisher","first-page":"65","DOI":"10.3390\/jimaging10030065","volume":"10","author":"FC Fizaine","year":"2024","unstructured":"Fizaine, F.C., Bard, P., Paindavoine, M., Robin, C., Bouy\u00e9, E., Lef\u00e8vre, R., Vinter, A.: Historical text line segmentation using deep learning algorithms: Mask-rcnn against u-net networks. J. Imaging 10(3), 65\u201365 (2024)","journal-title":"J. Imaging"},{"key":"526_CR31","doi-asserted-by":"publisher","unstructured":"Gader, T.B.A., Echi, A.K.: Unconstrained handwritten Arabic text-lines segmentation based on ar2u-net. volume 2020-September. IEEE, 9, pp. 349\u2013354 (2020). ISBN 978-1-7281-9966-5. https:\/\/doi.org\/10.1109\/ICFHR2020.2020.00070","DOI":"10.1109\/ICFHR2020.2020.00070"},{"issue":"8","key":"526_CR32","doi-asserted-by":"publisher","first-page":"8409","DOI":"10.1609\/aaai.v38i8.28683","volume":"38","author":"E-H Gao","year":"2024","unstructured":"Gao, E.-H., Huang, Y.-X., Wen-Chao, H., Zhu, X.-H., Dai, W.-Z.: Knowledge-enhanced historical document segmentation and recognition. Proc. AAAI Conf. Artif. Intell. 38(8), 8409\u20138416 (2024). https:\/\/doi.org\/10.1609\/aaai.v38i8.28683","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"526_CR33","doi-asserted-by":"publisher","unstructured":"Gatos, B., Antonacopoulos, A., Stamatopoulos, N.: Handwriting segmentation contest. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), 2, 1284\u20131288 (2007). https:\/\/doi.org\/10.1109\/ICDAR.2007.4377122","DOI":"10.1109\/ICDAR.2007.4377122"},{"key":"526_CR34","doi-asserted-by":"publisher","unstructured":"Gatos, B., Stamatopoulos, N., Louloudis, G.: Icdar 2009 handwriting segmentation contest. In: 2009 10th International Conference on Document Analysis and Recognition, pp. 1393\u20131397 (2009). https:\/\/doi.org\/10.1109\/ICDAR.2009.245","DOI":"10.1109\/ICDAR.2009.245"},{"key":"526_CR35","doi-asserted-by":"publisher","unstructured":"Gatos, B., Stamatopoulos, N., Louloudis, G.: Icfhr 2010 handwriting segmentation contest. In: 2010 12th International Conference on Frontiers in Handwriting Recognition, pp. 737\u2013742 (2010). https:\/\/doi.org\/10.1109\/ICFHR.2010.120","DOI":"10.1109\/ICFHR.2010.120"},{"key":"526_CR36","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63, 3\u201342 (2006)","journal-title":"Mach. Learn."},{"key":"526_CR37","doi-asserted-by":"publisher","unstructured":"Gr\u00fcning, T., Labahn, R., Diem, M., Kleber, F., Fiel, S.: Read-bad: A new dataset and evaluation scheme for baseline detection in archival documents. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 351\u2013356 (2018). https:\/\/doi.org\/10.1109\/DAS.2018.38","DOI":"10.1109\/DAS.2018.38"},{"key":"526_CR38","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10032-019-00332-1","volume":"22","author":"T Gr\u00fcning","year":"2019","unstructured":"Gr\u00fcning, T., Leifert, G., Strau\u00df, T., Michael, J., Labahn, R.: A two-stage method for text line detection in historical documents. Int. J. Doc. Anal. Recognit. 22, 285\u2013302 (2019). https:\/\/doi.org\/10.1007\/s10032-019-00332-1","journal-title":"Int. J. Doc. Anal. Recognit."},{"key":"526_CR39","unstructured":"Gr\u00fcning, T., Labahn, R., Diem, M., Kleber, F., Fiel, S.: TranskribusBaseLineEvaluationScheme, (2023). https:\/\/github.com\/Transkribus\/TranskribusBaseLineEvaluationScheme"},{"key":"526_CR40","doi-asserted-by":"publisher","unstructured":"Guerry, C., Couasnon, B., Lemaitre, A.: Combination of deep learning and syntactical approaches for the interpretation of interactions between text-lines and tabular structures in handwritten documents. IEEE 9, 858\u2013863 (2019). ISBN 978-1-7281-3014-9. https:\/\/doi.org\/10.1109\/ICDAR.2019.00142","DOI":"10.1109\/ICDAR.2019.00142"},{"key":"526_CR41","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":"526_CR42","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"issue":"102689","key":"526_CR43","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ipm.2021.102689","volume":"58","author":"H Pengfei","year":"2021","unstructured":"Pengfei, H., Wang, W., Li, Q., Wang, T.: Touching text line segmentation combined local baseline and connected component for Uchen Tibetan historical documents. Inf. Process. Manag. 58(102689), 11 (2021). https:\/\/doi.org\/10.1016\/j.ipm.2021.102689","journal-title":"Inf. Process. Manag."},{"key":"526_CR44","doi-asserted-by":"publisher","unstructured":"Hu, X., Wei, B., Gao, L., Wang, J.: Seghist: A general segmentation-based framework for Chinese historical document text line detection. In: Document Analysis and Recognition - ICDAR 2024: 18th International Conference, Athens, Greece, August 30 - September 4, 2024, Proceedings, Part III, pp. 391\u2013410. Springer, Berlin, Heidelberg (2024). ISBN 978-3-031-70542-7. https:\/\/doi.org\/10.1007\/978-3-031-70543-4_23","DOI":"10.1007\/978-3-031-70543-4_23"},{"key":"526_CR45","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10032-023-00438-7","volume-title":"Line Extraction in Handwritten Documents via Instance Segmentation","author":"A Islam","year":"2023","unstructured":"Islam, A., Anjum, T., Khan, N.: Line Extraction in Handwritten Documents via Instance Segmentation, vol. 26, pp. 335\u2013346. Springer, Berlin (2023). https:\/\/doi.org\/10.1007\/s10032-023-00438-7"},{"key":"526_CR46","doi-asserted-by":"publisher","first-page":"93672","DOI":"10.1109\/ACCESS.2021.3093568","volume":"9","author":"W Jia","year":"2021","unstructured":"Jia, W., Ma, C., Sun, L., Huo, Q.: Detecting text baselines in historical documents with baseline primitives. IEEE Access 9, 93672\u201393683 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3093568","journal-title":"IEEE Access"},{"key":"526_CR47","doi-asserted-by":"publisher","unstructured":"Jian, C., Jin, L., Liang, L., Liu, C.: HisDoc R-CNN: robust chinese historical document text line detection with dynamic rotational proposal network and iterative attention head, 428\u2013445. (2023). https:\/\/doi.org\/10.1007\/978-3-031-41676-7_25","DOI":"10.1007\/978-3-031-41676-7_25"},{"key":"526_CR48","doi-asserted-by":"publisher","first-page":"10703","DOI":"10.1007\/s11042-022-13709-y","volume":"82","author":"A Jindal","year":"2023","unstructured":"Jindal, A., Ghosh, R.: Text line segmentation in Indian ancient handwritten documents using faster r-cnn. Multimed. Tools Appl. 82, 10703\u201310722 (2023). https:\/\/doi.org\/10.1007\/s11042-022-13709-y","journal-title":"Multimed. Tools Appl."},{"key":"526_CR49","doi-asserted-by":"publisher","unstructured":"Kaddas, P., Gatos, B., Palaiologos, K., Christopoulou, K., Kritsis, K.: Text line detection and recognition of greek polytonic documents, 213\u2013225. (2023). https:\/\/doi.org\/10.1007\/978-3-031-41501-2_15","DOI":"10.1007\/978-3-031-41501-2_15"},{"key":"526_CR50","doi-asserted-by":"publisher","unstructured":"Kassis, M., El-Sana, J.: Learning free line detection in manuscripts using distance transform graph. IEEE, 9, pp. 222\u2013227 (2019). ISBN 978-1-7281-3014-9. https:\/\/doi.org\/10.1109\/ICDAR.2019.00044","DOI":"10.1109\/ICDAR.2019.00044"},{"key":"526_CR51","doi-asserted-by":"publisher","unstructured":"Kesiman, M.W.A., Valy, D., Burie, J.C., Paulus, E., Suryani, M., Hadi, S., Verleysen, M., Chhun, S., Ogier, J.-M.: Icfhr 2018 competition on document image analysis tasks for southeast Asian palm leaf manuscripts. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 483\u2013488 (2018). https:\/\/doi.org\/10.1109\/ICFHR-2018.2018.00090","DOI":"10.1109\/ICFHR-2018.2018.00090"},{"key":"526_CR52","doi-asserted-by":"publisher","unstructured":"Kiessling, B.: CurT: End-to-End Text Line Detection in Historical Documents with Transformers, volume 13639 LNCS, pp. 34\u201348. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-21648-0_3","DOI":"10.1007\/978-3-031-21648-0_3"},{"key":"526_CR53","doi-asserted-by":"publisher","unstructured":"Kiessling, B., Ezra, D.B., Miller, M.T.: Badam: A public dataset for baseline detection in Arabic-script manuscripts. In: Proceedings of the 5th International Workshop on Historical Document Imaging and Processing, HIP\u201919, pp. 13\u201318, New York, NY, USA. Association for Computing Machinery (2019). ISBN 9781450376686. https:\/\/doi.org\/10.1145\/3352631.3352648","DOI":"10.1145\/3352631.3352648"},{"key":"526_CR54","doi-asserted-by":"publisher","unstructured":"Kiessling, B., Ezra, D.S.B., Miller, M.T.: Badam: A public dataset for baseline detection in Arabic-script manuscripts. ACM, 9, pp. 13\u201318 (2019). ISBN 9781450376686. https:\/\/doi.org\/10.1145\/3352631.3352648","DOI":"10.1145\/3352631.3352648"},{"key":"526_CR55","unstructured":"Kitchenham, B.: Procedures for performing systematic reviews. Keele University, Keele, vol. 33, pp. 1\u201326 (2004). http:\/\/artemisa.unicauca.edu.co\/~ecaldon\/docs\/spi\/kitchenham_2004.pdf"},{"key":"526_CR56","doi-asserted-by":"publisher","unstructured":"Kodym, O., Hradi\u0161, M.: Page layout analysis system for unconstrained historic documents, volume 12822 LNCS, pp. 492\u2013506. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-86331-9_32","DOI":"10.1007\/978-3-030-86331-9_32"},{"key":"526_CR57","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/978-3-031-70546-5_24","volume-title":"Document Analysis and Recognition - ICDAR 2024","author":"A Kundu","year":"2024","unstructured":"Kundu, A., Bhattacharya, U.: Yolo assisted a* algorithm for robust line segmentation of degraded document images. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds.) Document Analysis and Recognition - ICDAR 2024, pp. 407\u2013424. Springer, Cham (2024) . (ISBN 978-3-031-70546-5)"},{"key":"526_CR58","doi-asserted-by":"publisher","unstructured":"Kurar\u00a0Barakat, B., El-Sana, J., Rabaev, I.: The pinkas dataset. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 732\u2013737 (2019).https:\/\/doi.org\/10.1109\/ICDAR.2019.00122","DOI":"10.1109\/ICDAR.2019.00122"},{"key":"526_CR59","doi-asserted-by":"publisher","unstructured":"Li, D., Wu, Y., Zhou, Y.: Linecounter: Learning handwritten text line segmentation by counting. volume 2021-September. IEEE, 9, pp. 929\u2013933 (2021). ISBN 978-1-6654-4115-5. https:\/\/doi.org\/10.1109\/ICIP42928.2021.9506664","DOI":"10.1109\/ICIP42928.2021.9506664"},{"key":"526_CR60","doi-asserted-by":"publisher","unstructured":"Li, H., Liu, C., Wang, J., Huang, M., Zhou, W., Jin, L. DTDT: highly accurate dense text line detection in historical documents via dynamic transformer, pp. 381\u2013396. (2023). https:\/\/doi.org\/10.1007\/978-3-031-41676-7_22","DOI":"10.1007\/978-3-031-41676-7_22"},{"key":"526_CR61","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.jvcir.2019.01.021","volume":"61","author":"Z Li","year":"2019","unstructured":"Li, Z., Wang, W., Chen, Y., Hao, Y.: A novel method of text line segmentation for historical document image of the Uchen Tibetan. J. Vis. Commun. Image Represent. 61, 23\u201332 (2019). https:\/\/doi.org\/10.1016\/j.jvcir.2019.01.021","journal-title":"J. Vis. Commun. Image Represent."},{"key":"526_CR62","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":"526_CR63","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"526_CR64","doi-asserted-by":"publisher","unstructured":"Ma, W., Zhang, H., Jin, L., Wu, S., Wang, J., Wang, Y.: Joint layout analysis, character detection and recognition for historical document digitization. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 31\u201336 (2020). https:\/\/doi.org\/10.1109\/ICFHR2020.2020.00017","DOI":"10.1109\/ICFHR2020.2020.00017"},{"key":"526_CR65","doi-asserted-by":"publisher","unstructured":"Mechi, O., Mehri, M., Ingold, R., Amara, N.E.B.: Text line segmentation in historical document images using an adaptive u-net architecture. IEEE, 9, pp. 369\u2013374 (2019). ISBN 978-1-7281-3014-9. https:\/\/doi.org\/10.1109\/ICDAR.2019.00066","DOI":"10.1109\/ICDAR.2019.00066"},{"key":"526_CR66","doi-asserted-by":"publisher","unstructured":"Mechi, O., Mehri, M., Ingold, R., Amara, N.E.B.: Combining deep and ad-hoc solutions to localize text lines in ancient Arabic document images. IEEE, 1, pp. 7759\u20137766 (2021). ISBN 978-1-7281-8808-9. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412562","DOI":"10.1109\/ICPR48806.2021.9412562"},{"key":"526_CR67","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s10032-021-00377-1","volume":"24","author":"O Mechi","year":"2021","unstructured":"Mechi, O., Mehri, M., Ingold, R., Amara, N.: A two-step framework for text line segmentation in historical Arabic and Latin document images. Int. J. Doc. Anal. Recognit. 24, 197\u2013218 (2021b). https:\/\/doi.org\/10.1007\/s10032-021-00377-1","journal-title":"Int. J. Doc. Anal. Recognit."},{"key":"526_CR68","doi-asserted-by":"publisher","unstructured":"Melnikov, A., Zagaynov, I.: Fast and Lightweight Text Line Detection on Historical Documents, volume 12116 LNCS, pp. 441\u2013450. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-57058-3_31","DOI":"10.1007\/978-3-030-57058-3_31"},{"key":"526_CR69","doi-asserted-by":"publisher","unstructured":"Monnier, T., Aubry, M.: docextractor: An off-the-shelf historical document element extraction. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 91\u201396 (2020).https:\/\/doi.org\/10.1109\/ICFHR2020.2020.00027","DOI":"10.1109\/ICFHR2020.2020.00027"},{"key":"526_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3474118","volume":"21","author":"J Mukherjee","year":"2022","unstructured":"Mukherjee, J., Parui, S.K., Roy, U.: An unsupervised and robust line and word segmentation method for handwritten and degraded printed document. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 21, 1\u201331 (2022). https:\/\/doi.org\/10.1145\/3474118","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process."},{"key":"526_CR71","doi-asserted-by":"publisher","unstructured":"Murdock, M., Reid, S., Hamilton, B., Reese, J.: Icdar 2015 competition on text line detection in historical documents. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1171\u20131175 (2015). https:\/\/doi.org\/10.1109\/ICDAR.2015.7333945","DOI":"10.1109\/ICDAR.2015.7333945"},{"key":"526_CR72","doi-asserted-by":"publisher","unstructured":"Nguyen, T.-N., Burie, J.-C., Le, T.-L., Schweyer, A.-V.: An effective method for text line segmentation in historical document images. volume 2022-August, pp. 1593\u20131599. IEEE, 8 2022. ISBN 978-1-6654-9062-7. https:\/\/doi.org\/10.1109\/ICPR56361.2022.9956617","DOI":"10.1109\/ICPR56361.2022.9956617"},{"key":"526_CR73","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s10032-022-00405-8","volume":"25","author":"K Nikolaidou","year":"2022","unstructured":"Nikolaidou, K., Seuret, M., Mokayed, H., Liwicki, M.: A survey of historical document image datasets. Int. J. Doc. Anal. Recognit. 25, 305\u2013338 (2022). https:\/\/doi.org\/10.1007\/s10032-022-00405-8","journal-title":"Int. J. Doc. Anal. Recognit."},{"key":"526_CR74","doi-asserted-by":"crossref","unstructured":"Oliveira, S.A., Seguin, B., Kaplan, F.: dhsegment: A generic deep-learning approach for document segmentation. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 7\u201312. IEEE (2018)","DOI":"10.1109\/ICFHR-2018.2018.00011"},{"key":"526_CR75","doi-asserted-by":"publisher","DOI":"10.1007\/s10032-024-00488-5","author":"\u0130 \u00d6z\u015feker","year":"2024","unstructured":"\u00d6z\u015feker, \u0130, Demir, A.A., \u00d6zkaya, U.: Gan-based text line segmentation method for challenging handwritten documents. Int. J. Doc. Anal. Recognit. (2024). https:\/\/doi.org\/10.1007\/s10032-024-00488-5","journal-title":"Int. J. Doc. Anal. Recognit."},{"key":"526_CR76","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.patcog.2019.05.031","volume":"94","author":"M Pastor","year":"2019","unstructured":"Pastor, M.: Text baseline detection, a single page trained system. Pattern Recognit. 94, 149\u2013161 (2019). https:\/\/doi.org\/10.1016\/j.patcog.2019.05.031","journal-title":"Pattern Recognit."},{"key":"526_CR77","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.patrec.2023.08.007","volume":"174","author":"E Paulus","year":"2023","unstructured":"Paulus, E., Burie, J.-C., Verbeek, F.J.: Text line extraction strategy for palm leaf manuscripts. Pattern Recognit. Lett. 174, 10\u201316 (2023). https:\/\/doi.org\/10.1016\/j.patrec.2023.08.007","journal-title":"Pattern Recognit. Lett."},{"key":"526_CR78","doi-asserted-by":"publisher","unstructured":"Pletschacher, S., Antonacopoulos, A.: The page (page analysis and ground-truth elements) format framework. In: 2010 20th International Conference on Pattern Recognition, pp. 257\u2013260 (2010). https:\/\/doi.org\/10.1109\/ICPR.2010.72","DOI":"10.1109\/ICPR.2010.72"},{"key":"526_CR79","doi-asserted-by":"crossref","unstructured":"Potanin, M., Dimitrov, D., Shonenkov, A., Bataev, V., Karachev, D., Novopoltsev, M.: Digital peter: dataset, competition and handwriting recognition methods (2021)","DOI":"10.1145\/3476887.3476892"},{"key":"526_CR80","doi-asserted-by":"publisher","unstructured":"Potanin, M., Dimitrov, D., Shonenkov, A., Bataev, V., Karachev, D., Novopoltsev, M., Chertok, A.: Digital peter: New dataset, competition and handwriting recognition methods. In: Proceedings of the 6th International Workshop on Historical Document Imaging and Processing, HIP\u201921, pp. 43\u201348, New York, NY, USA, (2021b). Association for Computing Machinery. ISBN 9781450386906. https:\/\/doi.org\/10.1145\/3476887.3476892","DOI":"10.1145\/3476887.3476892"},{"key":"526_CR81","unstructured":"PRImA Research Lab. RASM2019 ICDAR2019 Competition on Recognition of Historical Arabic Scientific Manuscripts. https:\/\/www.primaresearch.org\/RASM2019\/ (2019). Accessed 21.11.2023"},{"key":"526_CR82","doi-asserted-by":"publisher","unstructured":"Prusty, A., Aitha, S., Trivedi, A., Sarvadevabhatla, R.K.: Indiscapes: Instance segmentation networks for layout parsing of historical indic manuscripts. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 999\u20131006 (2019). https:\/\/doi.org\/10.1109\/ICDAR.2019.00164","DOI":"10.1109\/ICDAR.2019.00164"},{"key":"526_CR83","doi-asserted-by":"crossref","unstructured":"Rammal, H.G.: Systematic literature reviews: Steps and practical tips. In: Advancing methodologies of conducting literature review in management domain, vol.\u00a02, pp. 27\u201335. Emerald Publishing Limited (2023)","DOI":"10.1108\/S2754-586520230000002002"},{"key":"526_CR84","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"526_CR85","doi-asserted-by":"publisher","unstructured":"Saini, R., Dobson, D., Morrey, J., Liwicki, M., Simistira\u00a0Liwicki, F.: Icdar 2019 historical document reading challenge on large structured Chinese family records. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1499\u20131504 (2019). https:\/\/doi.org\/10.1109\/ICDAR.2019.00241","DOI":"10.1109\/ICDAR.2019.00241"},{"key":"526_CR86","doi-asserted-by":"publisher","unstructured":"S\u00e1nchez, J.A., Bosch, V., Romero, V., Depuydt Katrien, de Does, J.: Handwritten text recognition for historical documents in the transcriptorium project. DATeCH\u201914, pp. 111\u2013117, New York, NY, USA. Association for Computing Machinery. ISBN 9781450325882 (2014). https:\/\/doi.org\/10.1145\/2595188.2595193","DOI":"10.1145\/2595188.2595193"},{"key":"526_CR87","doi-asserted-by":"publisher","first-page":"297","DOI":"10.17605\/OSF.IO\/8DWSQ","volume":"4","author":"MM Sathik","year":"2021","unstructured":"Sathik, M.M., Ratheash, R.S.: Text line segmentation in Tamil language palm leaf manuscripts\u2014a novel approach. J. Tianjin Univ. Sci. Technol. 4, 297\u2013304 (2021). https:\/\/doi.org\/10.17605\/OSF.IO\/8DWSQ","journal-title":"J. Tianjin Univ. Sci. Technol."},{"key":"526_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118266","volume":"210","author":"P Shivakumara","year":"2022","unstructured":"Shivakumara, P., Jain, T., Pal, U., Surana, N., Antonacopoulos, A., Tong, L.: Text line segmentation from struck-out handwritten document images. Expert Syst. Appl. 210, 118266 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.118266","journal-title":"Expert Syst. Appl."},{"key":"526_CR89","doi-asserted-by":"publisher","unstructured":"Simistira, F., Seuret, M., Eichenberger, N., Garz, A., Liwicki, M., Ingold, R.: DIVA-HisDB: A precisely annotated large dataset of challenging medieval manuscripts. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 471\u2013476 (2016). https:\/\/doi.org\/10.1109\/ICFHR.2016.0093","DOI":"10.1109\/ICFHR.2016.0093"},{"key":"526_CR90","doi-asserted-by":"publisher","unstructured":"Simistira, F., Bouillon, M., Seuret, M., W\u00fcrsch, M., Alberti, M., Ingold, R., Liwicki, M.: Icdar2017 competition on layout analysis for challenging medieval manuscripts. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 01, pp. 1361\u20131370 (2017). https:\/\/doi.org\/10.1109\/ICDAR.2017.223","DOI":"10.1109\/ICDAR.2017.223"},{"key":"526_CR91","doi-asserted-by":"publisher","unstructured":"Stamatopoulos, N., Gatos, B., Louloudis, G., Pal, U., Alaei, A.: Icdar 2013 handwriting segmentation contest. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1402\u20131406 (2013). https:\/\/doi.org\/10.1109\/ICDAR.2013.283","DOI":"10.1109\/ICDAR.2013.283"},{"key":"526_CR92","unstructured":"Stutzmann, D., Aguilar, S.T., Chaffenet, P.: Home-alcar: aligned and annotated cartularies (2021)"},{"key":"526_CR93","doi-asserted-by":"publisher","unstructured":"S\u00e1nchez, J.A., Romero, V., Toselli, A.H., Vidal, E.: Icfhr2016 competition on handwritten text recognition on the read dataset. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 630\u2013635 (2016). https:\/\/doi.org\/10.1109\/ICFHR.2016.0120","DOI":"10.1109\/ICFHR.2016.0120"},{"key":"526_CR94","doi-asserted-by":"publisher","unstructured":"S\u00e1nchez, J.A., Romero, V., Toselli, A.H., Villegas, M., Vidal, E.: Icdar2017 competition on handwritten text recognition on the read dataset. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 01, pp. 1383\u20131388 (2017) https:\/\/doi.org\/10.1109\/ICDAR.2017.226","DOI":"10.1109\/ICDAR.2017.226"},{"key":"526_CR95","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/978-3-031-70543-4_13","volume-title":"Document Analysis and Recognition\u2014ICDAR 2024","author":"SM Unter","year":"2024","unstructured":"Unter, S.M.: Text line segmentation on ancient Egyptian papyri: Layout analysis with object detection networks and connected components. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds.) Document Analysis and Recognition\u2014ICDAR 2024, pp. 215\u2013232. Springer, Cham (2024) . (ISBN 978-3-031-70543-4)"},{"key":"526_CR96","doi-asserted-by":"publisher","unstructured":"Vadlamudi, N., Krishna, R., Sarvadevabhatla, R.K.: SeamFormer: High Precision Text Line Segmentation for Handwritten Documents, pp. 313\u2013331 (2023). https:\/\/doi.org\/10.1007\/978-3-031-41685-9_20","DOI":"10.1007\/978-3-031-41685-9_20"},{"key":"526_CR97","doi-asserted-by":"crossref","unstructured":"V\u00e9zina, H., Bournival, J.-S.: An overview of the Balsac population database. SOWING 183 (2020)","DOI":"10.2307\/jj.6445824.13"},{"key":"526_CR98","doi-asserted-by":"publisher","first-page":"4673","DOI":"10.1109\/ICPR48806.2021.9412624","volume":"1","author":"M Wodlinger","year":"2021","unstructured":"Wodlinger, M., Sablatnig, R.: Text baseline recognition using a recurrent convolutional neural network. IEEE 1, 4673\u20134679 (2021). https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412624","journal-title":"IEEE"},{"key":"526_CR99","doi-asserted-by":"crossref","unstructured":"Yang, X., Yumer, E., Asente, P., Kraley, M., Kifer, D., Giles, C.L.: Learning to extract semantic structure from documents using multimodal fully convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5315\u20135324 (2017)","DOI":"10.1109\/CVPR.2017.462"},{"issue":"6","key":"526_CR100","doi-asserted-by":"publisher","first-page":"nwad154","DOI":"10.1093\/nsr\/nwad154","volume":"10","author":"H Zhang","year":"2023","unstructured":"Zhang, H., Xu, X.: Introduction to the Greater Bay Area (Huangpu) international algorithm case competition. Natl. Sci. Rev. 10(6), nwad154 (2023). https:\/\/doi.org\/10.1093\/nsr\/nwad154","journal-title":"Natl. Sci. Rev."},{"key":"526_CR101","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s10032-019-00335-y","volume":"22","author":"Z Zhong","year":"2019","unstructured":"Zhong, Z., Sun, L., Huo, Q.: An anchor-free region proposal network for faster T-CNN-based text detection approaches. Int. J. Doc. Anal. Recognit. 22, 315\u2013327 (2019)","journal-title":"Int. J. Doc. Anal. Recognit."}],"container-title":["International Journal on Document Analysis and Recognition (IJDAR)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10032-025-00526-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10032-025-00526-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10032-025-00526-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:49:34Z","timestamp":1775803774000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10032-025-00526-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,7]]},"references-count":101,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["526"],"URL":"https:\/\/doi.org\/10.1007\/s10032-025-00526-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4786953\/v1","asserted-by":"object"}]},"ISSN":["1433-2833","1433-2825"],"issn-type":[{"value":"1433-2833","type":"print"},{"value":"1433-2825","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,7]]},"assertion":[{"value":"23 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}