{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:56:53Z","timestamp":1773248213216,"version":"3.50.1"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030586034","type":"print"},{"value":"9783030586041","type":"electronic"}],"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\/978-3-030-58604-1_5","type":"book-chapter","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T22:02:49Z","timestamp":1604354569000},"page":"70-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Table Structure Recognition Using Top-Down and Bottom-Up Cues"],"prefix":"10.1007","author":[{"given":"Sachin","family":"Raja","sequence":"first","affiliation":[]},{"given":"Ajoy","family":"Mondal","sequence":"additional","affiliation":[]},{"given":"C. V.","family":"Jawahar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Yang, X., Yumer, E., Asente, P., Kraley, M., Kifer, D., Lee Giles, C.: Learning to extract semantic structure from documents using multimodal fully convolutional neural networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.462"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Augusto Borges Oliveira, D., Palhares Viana, M.: Fast CNN-based document layout analysis. In: ICCV (2017)","DOI":"10.1109\/ICCVW.2017.142"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Yi, X., Gao, L., Liao, Y., Zhang, X., Liu, R., Jiang, Z.: CNN based page object detection in document images. In: ICDAR (2017)","DOI":"10.1109\/ICDAR.2017.46"},{"key":"5_CR4","unstructured":"Hu, J., Kashi, R.S., Lopresti, D.P., Wilfong, G.: Medium-independent table detection. In: Document Recognition and Retrieval VII (1999)"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Wang, Y., Phillips, I.T., Haralick, R.M.: Table structure understanding and its performance evaluation. Pattern Recogn. (2004)","DOI":"10.1016\/j.patcog.2004.01.012"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Nishida, K., Sadamitsu, K., Higashinaka, R., Matsuo, Y.: Understanding the semantic structures of tables with a hybrid deep neural network architecture. In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.10484"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: DeepDeSRT: deep learning for detection and structure recognition of tables in document images. In: ICDAR (2017)","DOI":"10.1109\/ICDAR.2017.192"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Bao, J., et al.: Table-to-text: describing table region with natural language. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11944"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Qasim, S.R., Mahmood, H., Shafait, F.: Rethinking table parsing using graph neural networks. In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00031"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Tensmeyer, C., Morariu, V., Price, B., Cohen, S., Martinezp, T.: Deep splitting and merging for table structure decomposition. In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00027"},{"key":"5_CR11","unstructured":"Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: TableBank: table benchmark for image-based table detection and recognition. In: ICDAR (2019)"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Paliwal, S.S., Vishwanath, D., Rahul, R., Sharma, M., Vig, L.: TableNet: deep learning model for end-to-end table detection and tabular data extraction from scanned document images. In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00029"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Zhong, X., ShafieiBavani, E., Yepes, A.J.: Image-based table recognition: data, model, and evaluation. arXiv (2019)","DOI":"10.1007\/978-3-030-58589-1_34"},{"key":"5_CR14","unstructured":"Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv (2019)"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Khan, S.A., Khalid, S.M.D., Shahzad, M.A., Shafait, F.: Table structure extraction with Bi-directional Gated Recurrent Unit networks. In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00220"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Siddiqui, S.A., Khan, P.I., Dengel, A., Ahmed, S.: Rethinking semantic segmentation for table structure recognition in documents. In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00225"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Xue, W., Li, Q., Tao, D.: ReS2TIM: reconstruct syntactic structures from table images. In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00125"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"G\u00f6bel, M., Hassan, T., Oro, E., Orsi, G.: ICDAR 2013 table competition. In: ICDAR (2013)","DOI":"10.1109\/ICDAR.2013.292"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Gao, L., et al.: ICDAR 2019 competition on table detection and recognition (cTDaR). In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00243"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Mondal, A., Lipps, P., Jawahar, C.V.: IIIT-AR-13K: a new dataset for graphical object detection in documents. In: DAS (2020)","DOI":"10.1007\/978-3-030-57058-3_16"},{"key":"5_CR21","unstructured":"Itonori, K.: Table structure recognition based on textblock arrangement and ruled line position. In: ICDAR (1993)"},{"key":"5_CR22","unstructured":"Green, E., Krishnamoorthy, M.: Recognition of tables using table grammars. In: Annual Symposium on Document Analysis and Information Retrieval (1995)"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Kieninger, T.G.: Table structure recognition based on robust block segmentation. In: Document Recognition V (1998)","DOI":"10.1117\/12.304642"},{"key":"5_CR24","unstructured":"Tupaj, S., Shi, Z., Chang, C.H., Alam, H.: Extracting Tabular Information from Text Files. Tufts University, Medford, USA, EECS Department (1996)"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Gilani, A., Qasim, S.R., Malik, I., Shafait, F.: Table detection using deep learning. In: ICDAR (2017)","DOI":"10.1109\/ICDAR.2017.131"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Dong, H., Liu, S., Han, S., Fu, Z., Zhang, D.: TableSense: spreadsheet table detection with convolutional neural networks. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.330169"},{"key":"5_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1007\/978-3-030-30645-8_27","volume-title":"Image Analysis and Processing \u2013 ICIAP 2019","author":"I Kavasidis","year":"2019","unstructured":"Kavasidis, I., et al.: A saliency-based convolutional neural network for table and chart detection in digitized documents. In: Ricci, E., Rota Bul\u00f2, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11752, pp. 292\u2013302. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30645-8_27"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Saha, R., Mondal, A., Jawahar, C.V.: Graphical object detection in document images. In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00018"},{"key":"5_CR29","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: DAS (2010)","DOI":"10.1145\/1815330.1815345"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Zanibbi, R., Blostein, D., Cordy, J.R.: Recognizing mathematical expressions using tree transformation. IEEE Trans. PAMI (2002)","DOI":"10.1109\/TPAMI.2002.1046157"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, J., Du, J., Dai, L.: Multi-scale attention with dense encoder for handwritten mathematical expression recognition. In: ICDAR (2018)","DOI":"10.1109\/ICPR.2018.8546031"},{"key":"5_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1007\/978-3-319-46478-7_41","volume-title":"Computer Vision \u2013 ECCV 2016","author":"N Siegel","year":"2016","unstructured":"Siegel, N., Horvitz, Z., Levin, R., Divvala, S., Farhadi, A.: FigureSeer: parsing result-figures in research papers. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 664\u2013680. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_41"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Tang, B., et al.: DeepChart: combining deep convolutional networks and deep belief networks in chart classification. Sig. Process. (2015)","DOI":"10.1016\/j.sigpro.2015.09.027"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Harit, G., Bansal, A.: Table detection in document images using header and trailer patterns. In: ICVGIP (2012)","DOI":"10.1145\/2425333.2425395"},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Gatos, B., Danatsas, D., Pratikakis, I., Perantonis, S.J.: Automatic table detection in document images. In: CVPR (2005)","DOI":"10.1007\/11551188_67"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Ohta, M., Yamada, R., Kanazawa, T., Takasu, A.: A cell-detection-based table-structure recognition method. In: ACM Symposium on Document Engineering (2019)","DOI":"10.1145\/3342558.3345412"},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00148"},{"key":"5_CR38","unstructured":"Adiga, D., Bhat, S.A., Shah, M.B., Vyeth, V.: Table structure recognition based on cell relationship, a bottom-up approach. In: RANLP (2019)"},{"key":"5_CR39","doi-asserted-by":"crossref","unstructured":"Riba, P., Dutta, A., Goldmann, L., Fornes, A., Ramos, O., Llados, J.: Table detection in invoice documents by graph neural networks. In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00028"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Hole\u010dek, M., Hoskovec, A., Baudi\u0161, P., Klinger, P.: Line-items and table understanding in structured documents. arXiv (2019)","DOI":"10.1109\/ICDARW.2019.40098"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Deng, L., Zhang, S., Balog, K.: Table2Vec: neural word and entity embeddings for table population and retrieval. In: SIGIR (2019)","DOI":"10.1145\/3331184.3331333"},{"key":"5_CR42","doi-asserted-by":"crossref","unstructured":"Le Vine, N., Zeigenfuse, M., Rowan, M.: Extracting tables from documents using conditional generative adversarial networks and genetic algorithms. In: IJCNN (2019)","DOI":"10.1109\/IJCNN.2019.8851886"},{"key":"5_CR43","doi-asserted-by":"crossref","unstructured":"Sage, C., Aussem, A., Elghazel, H., Eglin, V., Espinas, J.: Recurrent neural network approach for table field extraction in business documents. In: ICDAR (2019)","DOI":"10.1109\/ICDAR.2019.00211"},{"key":"5_CR44","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"5_CR45","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"5_CR46","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)"},{"key":"5_CR47","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: CVPR (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"5_CR48","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv (2015)"},{"key":"5_CR49","doi-asserted-by":"crossref","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. PAMI (2017)","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"5_CR50","doi-asserted-by":"crossref","unstructured":"Woo, S., Hwang, S., Jang, H.D., Kweon, I.S.: Gated bidirectional feature pyramid network for accurate one-shot detection. Mach. Vis. Appl. (2019)","DOI":"10.1007\/s00138-019-01017-9"},{"key":"5_CR51","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. (1997)","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"5_CR52","doi-asserted-by":"crossref","unstructured":"Qasim, S.R., Kieseler, J., Iiyama, Y., Pierini, M.: Learning representations of irregular particle-detector geometry with distance-weighted graph networks. arXiv (2019)","DOI":"10.1140\/epjc\/s10052-019-7113-9"},{"key":"5_CR53","doi-asserted-by":"crossref","unstructured":"Smith, R.: An overview of the Tesseract OCR engine. In: ICDAR (2007)","DOI":"10.1109\/ICDAR.2007.4376991"},{"key":"5_CR54","doi-asserted-by":"crossref","unstructured":"Lin, T., et al.: Microsoft COCO: common objects in context. CoRR (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"5_CR55","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: AMACL (2002)","DOI":"10.3115\/1073083.1073135"},{"key":"5_CR56","doi-asserted-by":"crossref","unstructured":"Vedantam, R., Lawrence Zitnick, C., Parikh, D.: CIDEr: consensus-based image description evaluation. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299087"},{"key":"5_CR57","unstructured":"Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out (2004)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58604-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:08:44Z","timestamp":1730506124000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58604-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030586034","9783030586041"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58604-1_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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","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":"7","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":"The conference was held virtually due to the COVID-19 pandemic.","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)"}}]}}