{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:03:31Z","timestamp":1760598211132,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031250682"},{"type":"electronic","value":"9783031250699"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25069-9_17","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:15:46Z","timestamp":1676333746000},"page":"253-262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Self-paced Learning to\u00a0Improve Text Row Detection in\u00a0Historical Documents with\u00a0Missing Labels"],"prefix":"10.1007","author":[{"given":"Mihaela","family":"G\u0103man","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lida","family":"Ghadamiyan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radu Tudor","family":"Ionescu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marius","family":"Popescu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"17_CR1","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"17_CR2","unstructured":"Cristea, D., P\u0103durariu, C., Rebeja, P., Onofrei, M.: From Scan to Text. Methodology, Solutions and Perspectives of Deciphering Old Cyrillic Romanian Documents into the Latin Script. In: Knowledge, Language, Models, pp. 38\u201356 (2020)"},{"key":"17_CR3","unstructured":"Cristea, D., Rebeja, P., P\u0103durariu, C., Onofrei, M., Scutelnicu, A.: Data Structure and Acquisition in DeLORo - a Technology for Deciphering Old Cyrillic-Romanian Documents. In: Proceedings of ConsILR (2022)"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Diem, M., Kleber, F., Fiel, S., Gr\u00fcning, T., Gatos, B.: cBAD: ICDAR2017 competition on baseline detection. In: Proceedings of ICDAR, pp. 1355\u20131360 (2017)","DOI":"10.1109\/ICDAR.2017.222"},{"issue":"3","key":"17_CR5","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. Document Anal. Recogn. (IJDAR) 22(3), 285\u2013302 (2019). https:\/\/doi.org\/10.1007\/s10032-019-00332-1","journal-title":"Int. J. Document Anal. Recogn. (IJDAR)"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Jiang, L., Meng, D., Zhao, Q., Shan, S., Hauptmann, A.: Self-paced curriculum learning. In: Proceedings of AAAI, pp. 2694\u20132700 (2015)","DOI":"10.1609\/aaai.v29i1.9608"},{"key":"17_CR7","unstructured":"Kumar, M.P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: Proceedings of NIPS, vol. 23, pp. 1189\u20131197 (2010)"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Lin, W., Gao, J., Wang, Q., Li, X.: Pixel-level self-paced learning for super-resolution. In: Proceedings of ICASSP, pp. 2538\u20132542 (2020)","DOI":"10.1109\/ICASSP40776.2020.9054293"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Liu, W., et al.: SSD: single Shot MultiBox Detector. In: Proceedings of ECCV, pp. 21\u201337 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"10","key":"17_CR10","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3390\/jimaging6100110","volume":"6","author":"F Lombardi","year":"2020","unstructured":"Lombardi, F., Marinai, S.: Deep learning for historical document analysis and recognition-a survey. J. Imaging 6(10), 110 (2020)","journal-title":"J. Imaging"},{"issue":"23","key":"17_CR11","doi-asserted-by":"publisher","first-page":"17209","DOI":"10.1007\/s00521-020-04910-x","volume":"32","author":"J Mart\u00ednek","year":"2020","unstructured":"Mart\u00ednek, J., Lenc, L., Kr\u00e1l, P.: Building an efficient OCR system for historical documents with little training data. Neural Comput. Appl. 32(23), 17209\u201317227 (2020). https:\/\/doi.org\/10.1007\/s00521-020-04910-x","journal-title":"Neural Comput. Appl."},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Mart\u00ednek, J., Lenc, L., Kr\u00e1l, P., Nicolaou, A., Christlein, V.: Hybrid Training Data for Historical Text OCR. In: Proceedings of ICDAR, pp. 565\u2013570 (2019)","DOI":"10.1109\/ICDAR.2019.00096"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Mechi, O., Mehri, M., Ingold, R., Amara, N.E.B.: Text line segmentation in historical document images using an adaptive U-Net architecture. In: Proceedings of ICDAR, pp. 369\u2013374 (2019)","DOI":"10.1109\/ICDAR.2019.00066"},{"key":"17_CR14","doi-asserted-by":"crossref","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. In: Proceedings of ICPR, pp. 7759\u20137766 (2021)","DOI":"10.1109\/ICPR48806.2021.9412562"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Melnikov, A., Zagaynov, I.: Fast and lightweight text line detection on historical documents. In: Proceedings of DAS, pp. 441\u2013450 (2020)","DOI":"10.1007\/978-3-030-57058-3_31"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Neudecker, C., et al.: OCR-D: an end-to-end open source OCR framework for historical printed documents. In: Proceedings of DATeCH, pp. 53\u201358 (2019)","DOI":"10.1145\/3322905.3322917"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Nunamaker, B., Bukhari, S.S., Borth, D., Dengel, A.: A Tesseract-based OCR framework for historical documents lacking ground-truth text. In: Proceedings of ICIP, pp. 3269\u20133273 (2016)","DOI":"10.1109\/ICIP.2016.7532964"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: Unified, Real-Time Object Detection. In: Proceedings of CVPR, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"17_CR19","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"17_CR20","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of NIPS, pp. 91\u201399 (2015)"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Ristea, N.C., Ionescu, R.T.: Self-paced ensemble learning for speech and audio classification. In: Proceedings of INTERSPEECH, pp. 2836\u20132840 (2021)","DOI":"10.21437\/Interspeech.2021-155"},{"key":"17_CR22","unstructured":"Soviany, P., Ionescu, R.T., Rota, P., Sebe, N.: Curriculum learning: A survey. arXiv preprint arXiv:2101.10382 (2021)"},{"key":"17_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103166","volume":"204","author":"P Soviany","year":"2021","unstructured":"Soviany, P., Ionescu, R.T., Rota, P., Sebe, N.: Curriculum self-paced learning for cross-domain object detection. Comput. Vis. Image Underst. 204, 103166 (2021)","journal-title":"Comput. Vis. Image Underst."},{"key":"17_CR24","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.patrec.2018.06.029","volume":"132","author":"W Zheng","year":"2020","unstructured":"Zheng, W., Zhu, X., Wen, G., Zhu, Y., Yu, H., Gan, J.: Unsupervised feature selection by self-paced learning regularization. Pattern Recogn. Lett. 132, 4\u201311 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, P., Du, L., Liu, X., Shen, Y.D., Fan, M., Li, X.: Self-paced clustering ensemble. IEEE Transactions on Neural Networks and Learning Systems (2020)","DOI":"10.1109\/TNNLS.2020.2984814"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25069-9_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:18:45Z","timestamp":1709831925000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25069-9_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250682","9783031250699"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25069-9_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}