{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:05:14Z","timestamp":1743059114317,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030570576"},{"type":"electronic","value":"9783030570583"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-57058-3_15","type":"book-chapter","created":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T23:06:05Z","timestamp":1597359965000},"page":"199-215","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images"],"prefix":"10.1007","author":[{"given":"\u00c1ngela","family":"Casado-Garc\u00eda","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2081-7523","authenticated-orcid":false,"given":"C\u00e9sar","family":"Dom\u00ednguez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4775-1306","authenticated-orcid":false,"given":"J\u00f3nathan","family":"Heras","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0538-4579","authenticated-orcid":false,"given":"Eloy","family":"Mata","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3576-0889","authenticated-orcid":false,"given":"Vico","family":"Pascual","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,14]]},"reference":[{"key":"15_CR1","unstructured":"Abdulla, W.: Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow (2017). https:\/\/github.com\/matterport\/Mask_RCNN"},{"key":"15_CR2","unstructured":"Alexey, A.B.: YOLO darknet (2018). https:\/\/github.com\/AlexeyAB\/darknet"},{"key":"15_CR3","unstructured":"Cesari, F., et al.: Trainable table location in document images. In: 16th International Conference on Pattern Recognition, ICPR 2002, vol. 3, p. 30236. ACM (2002)"},{"key":"15_CR4","unstructured":"Chen, T., et al.: MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs\/1512.01274 (2015). http:\/\/arxiv.org\/abs\/1512.01274"},{"key":"15_CR5","unstructured":"Colaboratory team: Google colaboratory (2017). https:\/\/colab.research.google.com"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Costa e Silva, A.: Learning rich hidden Markov models in document analysis: table location. In: 10th International Conference on Document Analysis and Recognition, ICDAR 2010, pp. 843\u2013847. IEEE (2009)","DOI":"10.1109\/ICDAR.2009.185"},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/978-0-85729-859-1_20","volume-title":"Handbook of Document Image Processing and Recognition","author":"B Co\u00fcasnon","year":"2014","unstructured":"Co\u00fcasnon, B., Lemaitre, A.: Recognition of tables and forms. In: Doermann, D., Tombre, K. (eds.) Handbook of Document Image Processing and Recognition, pp. 647\u2013677. Springer, London (2014). https:\/\/doi.org\/10.1007\/978-0-85729-859-1_20"},{"issue":"2\u20133","key":"15_CR8","first-page":"647","volume":"8","author":"DW Embley","year":"2006","unstructured":"Embley, D.W., et al.: Table-processing paradigms: a research survey. Int. J. Doc. Anal. Recogn. 8(2\u20133), 647\u2013677 (2006)","journal-title":"Int. J. Doc. Anal. Recogn."},{"issue":"1","key":"15_CR9","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., et al.: The Pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98\u2013136 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Gao, L., Yi, X., Jiang, Z., Hao, L., Tang, Z.: ICDAR2017 competition on page object detection. In: 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017, pp. 1417\u20131422 (2017)","DOI":"10.1109\/ICDAR.2017.231"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Gilani, A., et al.: Table detection using deep learning. In: 14th International Conference on Document Analysis and Recognition, ICDAR 2017, pp. 771\u2013776. IEEE (2017)","DOI":"10.1109\/ICDAR.2017.131"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Girshick, R., et al.: Accurate object detection and semantic segmentation. In: 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2014, pp. 580\u2013587. IEEE (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Gobel, M.C., Hassan, T., Oro, E., Orsi, G.: ICDAR2013 table competition. In: 12th ICDAR Robust Reading Competition, ICDAR 2013, pp. 1449\u20131453. IEEE (2013)","DOI":"10.1109\/ICDAR.2013.292"},{"key":"15_CR14","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http:\/\/www.deeplearningbook.org"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Hao, L., et al.: A table detection method for PDF documents based on convolutional neural networks. In: 12th International Workshop on Document Analysis Systems, DAS 2016, pp. 287\u2013292. IEEE (2016)","DOI":"10.1109\/DAS.2016.23"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Hirayama, Y.: A method for table structure analysis using DP matching. In: 3rd International Conference on Document Analysis and Recognition, ICDAR 1995, pp. 583\u2013586. IEEE (1995)","DOI":"10.1109\/ICDAR.1995.601964"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: A YOLO-based table detection method. In: 15th International Conference on Document Analysis and Recognition, ICDAR 2019 (2019)","DOI":"10.1109\/ICDAR.2019.00135"},{"key":"15_CR18","unstructured":"Institute of Computer Science and Technology of Peking University and Institute of Digital Publishing of Founder R&D Center, China: Marmot dataset for table recognition (2011). http:\/\/www.icst.pku.edu.cn\/cpdp\/sjzy\/index.htm"},{"key":"15_CR19","unstructured":"Jianying, H., et al.: Medium-independent table detection. In: Document Recognition and Retrieval VII. vol. 3967, pp. 583\u2013586. International Society for Optics and Photonics (1999)"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Kasar, T., et al.: Learning to detect tables in scanned document images using line information. In: 12th International Conference on Document Analysis and Recognition, ICDAR 2013, pp. 1185\u20131189. IEEE (2013)","DOI":"10.1109\/ICDAR.2013.240"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Kerwat, M., George, R., Shujaee, K.: Detecting knowledge artifacts in scientific document images - comparing deep learning architectures. In: 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018, pp. 147\u2013152. IEEE (2018)","DOI":"10.1109\/SNAMS.2018.8554818"},{"key":"15_CR22","unstructured":"Kluyver, T., et al.: Jupyter notebooks \u2013 a publishing format for reproducible computational workflows. In: 20th International Conference on Electronic Publishing, pp. 87\u201390. IOS Press (2016)"},{"key":"15_CR23","unstructured":"Li, M., et al.: TableBank: Table Benchmark for Image-based Table Detection and Recognition. CoRR abs\/1903.01949 (2019). http:\/\/arxiv.org\/abs\/1903.01949"},{"key":"15_CR24","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., Doll\u00e1r., P.: Keras retinanet (2017). https:\/\/github.com\/fizyr\/keras-retinanet"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Focal loss for dense object detection. In: 16th International Conference on Computer Vision, ICCV 2017, pp. 2999\u20133007 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"15_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"15_CR27","unstructured":"Oliveira, D.A.B., Viana, M.P.: Fast CNN-based document layout analysis. In: 14th International Conference on Computer Vision Workshops, ICCVW 2017, pp. 1173\u20131180. IEEE (2017)"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Oro, E., Ruffolo, M.: PDF-TREX: an approach for recognizing and extracting tables from PDF documents. In: 10th International Conference on Document Analysis and Recognition, ICDAR 2009, pp. 906\u2013910. IEEE (2009)","DOI":"10.1109\/ICDAR.2009.12"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Razavian, A.S., et al.: CNN features off-the-shelf: an astounding baseline for recognition. In: 27th Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014, pp. 512\u2013519 (2014)","DOI":"10.1109\/CVPRW.2014.131"},{"key":"15_CR30","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. CoRR abs\/1804.02767 (2018). http:\/\/arxiv.org\/abs\/1804.02767"},{"key":"15_CR31","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28, pp. 91\u201399 (2015)"},{"key":"15_CR32","unstructured":"Rosebrock, A.: Deep Learning for Computer Vision with Python. PyImageSearch (2018). https:\/\/www.pyimagesearch.com\/"},{"issue":"3","key":"15_CR33","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Schreiber, S., et al.: DeepDeSRT: deep learning for detection and structure recognition of tables in document images. In: 14th International Conference on Document Analysis and Recognition, ICDAR 2017, pp. 1162\u20131167. IEEE (2017)","DOI":"10.1109\/ICDAR.2017.192"},{"key":"15_CR35","doi-asserted-by":"crossref","unstructured":"Shahab, A., Shafait, F., Kieninger, T., Dengel, A.: An open approach towards the benchmarking of table structure recognition systems. In: 9th IAPR International Workshop on Document Analysis Systems, DAS 2010, pp. 113\u2013120 (2010)","DOI":"10.1145\/1815330.1815345"},{"key":"15_CR36","doi-asserted-by":"publisher","first-page":"74151","DOI":"10.1109\/ACCESS.2018.2880211","volume":"6","author":"SA Siddiqui","year":"2018","unstructured":"Siddiqui, S.A., et al.: DeCNT: deep deformable CNN for table detection. IEEE Access 6, 74151\u201374161 (2018)","journal-title":"IEEE Access"},{"key":"15_CR37","unstructured":"Suen, C.Y., et al.: ICDAR2019 Table Competition (2019). http:\/\/icdar2019.org\/"},{"issue":"1","key":"15_CR38","first-page":"1","volume":"7","author":"R Zanibbi","year":"2004","unstructured":"Zanibbi, R., Blostein, D., Cordy, J.R.: A survey of table recognition. Document Anal. Recogn. 7(1), 1\u201316 (2004)","journal-title":"Document Anal. Recogn."}],"container-title":["Lecture Notes in Computer Science","Document Analysis Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-57058-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:05:55Z","timestamp":1723593955000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-57058-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030570576","9783030570583"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-57058-3_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Document Analysis Systems","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"das2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iapr.org\/das2020","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57","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":"40","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":"70% - 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":"2.86","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":"1.01","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":"Due to the Corona pandemic the conference was held as a virtual event.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}