{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:35:26Z","timestamp":1764333326649,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031416781"},{"type":"electronic","value":"9783031416798"}],"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\/978-3-031-41679-8_11","type":"book-chapter","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:02:59Z","timestamp":1692342179000},"page":"185-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Key-Value Information Extraction from\u00a0Full Handwritten Pages"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6174-9865","authenticated-orcid":false,"given":"Sol\u00e8ne","family":"Tarride","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0618-7852","authenticated-orcid":false,"given":"M\u00e9lodie","family":"Boillet","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7508-4080","authenticated-orcid":false,"given":"Christopher","family":"Kermorvant","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"key":"11_CR1","unstructured":"Akbik, A., Bergmann, T., Blythe, D., Rasul, K., Schweter, S., Vollgraf, R.: FLAIR: an easy-to-use framework for state-of-the-art NLP. In: 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)(Demonstrations), pp. 54\u201359 (2019)"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.patrec.2020.05.001","volume":"136","author":"M Carbonell","year":"2020","unstructured":"Carbonell, M., Forn\u00e9s, A., Villegas, M., Llad\u00f3s, J.: A neural model for text localization, transcription and named entity recognition in full pages. Pattern Recogn. Lett. 136, 219\u2013227 (2020). https:\/\/doi.org\/10.1016\/j.patrec.2020.05.001","journal-title":"Pattern Recogn. Lett."},{"key":"11_CR3","doi-asserted-by":"publisher","unstructured":"Carbonell, M., Villegas, M., Forn\u00e9s, A., Llad\u00f3s, J.: Joint recognition of handwritten text and named entities with a neural end-to-end model. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 399\u2013404. IEEE Computer Society, Los Alamitos, CA, USA, April 2018. https:\/\/doi.org\/10.1109\/DAS.2018.52","DOI":"10.1109\/DAS.2018.52"},{"key":"11_CR4","doi-asserted-by":"publisher","unstructured":"Constum, T., et al.: Recognition and information extraction in historical handwritten tables: toward understanding early 20th century Paris census. In: 15th International Workshop on Document Analysis Systems (DAS), pp. 143\u2013157, May 2022. https:\/\/doi.org\/10.1007\/978-3-031-06555-2_10","DOI":"10.1007\/978-3-031-06555-2_10"},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2023.3235826","volume":"45","author":"D Coquenet","year":"2023","unstructured":"Coquenet, D., Chatelain, C., Paquet, T.: DAN: a segmentation-free document attention network for handwritten document recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45, 1\u201317 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3235826","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/TPAMI.2022.3144899","volume":"45","author":"D Coquenet","year":"2023","unstructured":"Coquenet, D., Chatelain, C., Paquet, T.: End-to-end handwritten paragraph text recognition using a vertical attention network. IEEE Trans. Pattern Anal. Mach. Intell. 45, 508\u2013524 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2022.3144899","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR7","doi-asserted-by":"publisher","unstructured":"Davis, B., Morse, B., Price, B., Tensmeyer, C., Wigington, C., Morariu, V.: End-to-end Document Recognition and Understanding with Dessurt (2022). https:\/\/doi.org\/10.48550\/ARXIV.2203.16618","DOI":"10.48550\/ARXIV.2203.16618"},{"key":"11_CR8","doi-asserted-by":"publisher","unstructured":"Forn\u00e9s, A., Romero, V., Baro, A., Toledo, J., S\u00e1nchez, J.A., Vidal, E., Llad\u00f3s, J.: ICDAR2017 competition on information extraction in historical handwritten records. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 1389\u20131394, November 2017. https:\/\/doi.org\/10.1109\/ICDAR.2017.227","DOI":"10.1109\/ICDAR.2017.227"},{"key":"11_CR9","doi-asserted-by":"publisher","unstructured":"Honnibal, M., Montani, I., Van Landeghem, S., Boyd, A.: spaCy: Industrial-strength Natural Language Processing in Python (2020). https:\/\/doi.org\/10.5281\/zenodo.1212303","DOI":"10.5281\/zenodo.1212303"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Kang, L., Toledo, J.I., Riba, P., Villegas, M., Forn\u00e9s, A., Rusi\u00f1ol, M.: Convolve, attend and spell: an attention-based sequence-to-sequence model for handwritten word recognition. In: German Conference on Pattern Recognition, pp. 459\u2013472 (2019)","DOI":"10.1007\/978-3-030-12939-2_32"},{"key":"11_CR11","doi-asserted-by":"publisher","unstructured":"Kiessling, B., Tissot, R., Stokes, P., St\u00f6kl Ben Ezra, D.: eScriptorium: an open source platform for historical document analysis. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW) (2019). https:\/\/doi.org\/10.1109\/ICDARW.2019.10032","DOI":"10.1109\/ICDARW.2019.10032"},{"key":"11_CR12","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, 110 (2020)","journal-title":"J. Imaging"},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s100320200071","volume":"5","author":"UV Marti","year":"2002","unstructured":"Marti, U.V., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition. Int. J. Doc. Anal. Recogn. 5, 39\u201346 (2002). https:\/\/doi.org\/10.1007\/s100320200071","journal-title":"Int. J. Doc. Anal. Recogn."},{"key":"11_CR14","unstructured":"Miret, B., Kermorvant, C.: Nerval: a python library for named-entity recognition evaluation on noisy texts (2021). http:\/\/gitlab.com\/teklia\/ner\/nerval"},{"key":"11_CR15","doi-asserted-by":"publisher","unstructured":"Monroc, C.B., Miret, B., Bonhomme, M.L., Kermorvant, C.: A comprehensive study of open-source libraries for named entity recognition on handwritten historical documents. In: Document Analysis Systems, pp. 429\u2013444 (2022). https:\/\/doi.org\/10.1007\/978-3-031-06555-2_29","DOI":"10.1007\/978-3-031-06555-2_29"},{"key":"11_CR16","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1108\/JD-07-2018-0114","volume":"75","author":"G Muehlberger","year":"2019","unstructured":"Muehlberger, G., et al.: Transforming scholarship in the archives through handwritten text recognition: Transkribus as a case study. J. Doc. 75, 954\u2013976 (2019). https:\/\/doi.org\/10.1108\/JD-07-2018-0114","journal-title":"J. Doc."},{"key":"11_CR17","unstructured":"Prasad, A., D\u00e9jean, H., Meunier, J., Weidemann, M., Michael, J., Leifert, G.: Bench-marking information extraction in semi-structured historical handwritten records. In: CoRR (2018). http:\/\/arxiv.org\/abs\/1807.06270"},{"key":"11_CR18","doi-asserted-by":"publisher","unstructured":"Puigcerver, J.: Are multidimensional recurrent layers really necessary for handwritten text recognition? In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, pp. 67\u201372 (2017). https:\/\/doi.org\/10.1109\/ICDAR.2017.20","DOI":"10.1109\/ICDAR.2017.20"},{"key":"11_CR19","doi-asserted-by":"publisher","unstructured":"Qi, P., Zhang, Y., Zhang, Y., Bolton, J., Manning, C.D.: Stanza: a python natural language processing toolkit for many human languages. In: 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 101\u2013108, January 2020. https:\/\/doi.org\/10.18653\/v1\/2020.acl-demos.14","DOI":"10.18653\/v1\/2020.acl-demos.14"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1016\/j.patcog.2012.11.024","volume":"46","author":"V Romero","year":"2013","unstructured":"Romero, V., et al.: The ESPOSALLES database: an ancient marriage license corpus for off-line handwriting recognition. Pattern Recogn. 46, 1658\u20131669 (2013). https:\/\/doi.org\/10.1016\/j.patcog.2012.11.024","journal-title":"Pattern Recogn."},{"key":"11_CR21","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.patrec.2021.11.010","volume":"155","author":"AC Rouhou","year":"2022","unstructured":"Rouhou, A.C., Dhiaf, M., Kessentini, Y., Salem, S.B.: Transformer-based approach for joint handwriting and named entity recognition in historical document. Pattern Recogn. Lett. 155, 128\u2013134 (2022). https:\/\/doi.org\/10.1016\/j.patrec.2021.11.010","journal-title":"Pattern Recogn. Lett."},{"key":"11_CR22","unstructured":"Rowtula, V., Krishnan, P., Jawahar, C.V.: POS tagging and named entity recognition on handwritten documents. In: Proceedings of the 15th International Conference on Natural Language Processing (2018)"},{"key":"11_CR23","doi-asserted-by":"publisher","unstructured":"Tarride, S., Lemaitre, A., Co\u00fcasnon, B., Tardivel, S.: A comparative study of information extraction strategies using an attention-based neural network. In: Document Analysis Systems, pp. 644\u2013658 (2022). https:\/\/doi.org\/10.1007\/978-3-031-06555-2_43","DOI":"10.1007\/978-3-031-06555-2_43"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Tarridea, S., et al.: Large-scale genealogical information extraction from handwritten Quebec parish records. Int. J. Document Anal. Recogn. (2023)","DOI":"10.21203\/rs.3.rs-2260181\/v1"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.patcog.2018.08.020","volume":"86","author":"JI Toledo","year":"2019","unstructured":"Toledo, J.I., Carbonell, M., Forn\u00e9s, A., Llad\u00f3s, J.: Information extraction from historical handwritten document images with a context-aware neural model. Pattern Recogn. 86, 27\u201336 (2019). https:\/\/doi.org\/10.1016\/j.patcog.2018.08.020","journal-title":"Pattern Recogn."},{"key":"11_CR26","doi-asserted-by":"publisher","unstructured":"T\u00fcselmann, O., Wolf, F., Fink, G.A.: Are end-to-end systems really necessary for ner on handwritten document images? In: Document Analysis and Recognition - ICDAR 2021, pp. 808\u2013822 (2021). https:\/\/doi.org\/10.1007\/978-3-030-86331-9_52","DOI":"10.1007\/978-3-030-86331-9_52"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Vidal, E., et al.: The Carabela project and manuscript collection: large-scale probabilistic indexing and content-based classification. In: In proceedings of the 17th International Conference on Frontiers in Handwriting Recognition (ICFHR 2020) (2020)","DOI":"10.1109\/ICFHR2020.2020.00026"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Yousef, M., Bishop, T.: OrigamiNet: weakly-supervised, segmentation-free, one-step, full page text recognition by learning to unfold. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14698\u201314707, June 2020","DOI":"10.1109\/CVPR42600.2020.01472"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition - ICDAR 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41679-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:24:33Z","timestamp":1692343473000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41679-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031416781","9783031416798"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41679-8_11","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":"19 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Jos\u00e9, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2023","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":"icdar2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdar2023.org\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"316","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":"154","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":"49% - 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":"2.89","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":"1.50","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Number and type of other papers accepted : IJDAR track papers","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)"}}]}}