{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T09:15:56Z","timestamp":1764494156087,"version":"3.44.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030863364"},{"type":"electronic","value":"9783030863371"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86337-1_33","type":"book-chapter","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T20:48:12Z","timestamp":1630702092000},"page":"494-509","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Handwriting Recognition with Novelty"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0529-9190","authenticated-orcid":false,"given":"Derek S.","family":"Prijatelj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2433-5257","authenticated-orcid":false,"given":"Samuel","family":"Grieggs","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5354-8254","authenticated-orcid":false,"given":"Futoshi","family":"Yumoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4942-4619","authenticated-orcid":false,"given":"Eric","family":"Robertson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9649-8074","authenticated-orcid":false,"given":"Walter J.","family":"Scheirer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,2]]},"reference":[{"key":"33_CR1","unstructured":"Augustin, E., Carr\u00e9, M., Grosicki, E., Brodin, J.M., Geoffrois, E., Pr\u00eateux, F.: RIMES evaluation campaign for handwritten mail processing. In: IWFHR (2006)"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Bendale, A., Boult, T.E.: Towards open set deep networks. In: IEEE CVPR (2016)","DOI":"10.1109\/CVPR.2016.173"},{"key":"33_CR3","unstructured":"Boult, T.E., et al.: A Unifying Framework for Formal Theories of Novelty:Framework, Examples and Discussion. arXiv:2012.04226 [cs], December 2020. http:\/\/arxiv.org\/abs\/2012.04226"},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Boult, T.E., et al.: A unifying framework for formal theories of novelty: framework, examples and discussion. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i17.17766"},{"key":"33_CR5","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE CVPR (2005)"},{"key":"33_CR6","unstructured":"DARPA: Teaching AI systems to adapt to dynamic environments (2019). https:\/\/www.darpa.mil\/news-events\/2019-02-14. Accessed 11 Jan 2021"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Fei, G., Liu, B.: Breaking the closed world assumption in text classification. In: NAACL-HLT (2016)","DOI":"10.18653\/v1\/N16-1061"},{"key":"33_CR8","doi-asserted-by":"publisher","unstructured":"Fiel, S., Kleber, F., Diem, M., Christlein, V., Louloudis, G., Nikos, S., Gatos, B.: ICDAR2017 competition on historical document writer identification (Historical-WI). In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). vol. 01, pp. 1377\u20131382 (November 2017). https:\/\/doi.org\/10.1109\/ICDAR.2017.225. iSSN: 2379-2140","DOI":"10.1109\/ICDAR.2017.225"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: ICML (2006)","DOI":"10.1145\/1143844.1143891"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Langley, P.: Open-world learning for radically autonomous agents. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i09.7078"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"van Lit, L.: Paleography: between erudition and computation. In: Among Digitized Manuscripts. Philology, Codicology, Paleography in a Digital World, pp. 102\u2013131. Brill (2019)","DOI":"10.1163\/9789004400351_006"},{"issue":"5","key":"33_CR13","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TPAMI.2006.102","volume":"28","author":"LM Lorigo","year":"2006","unstructured":"Lorigo, L.M., Govindaraju, V.: Offline Arabic handwriting recognition: a survey. IEEE T-PAMI 28(5), 712\u2013724 (2006)","journal-title":"IEEE T-PAMI"},{"issue":"1","key":"33_CR14","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. Recognit. 5(1), 39\u201346 (2002)","journal-title":"Int. J. Doc. Anal. Recognit."},{"key":"33_CR15","unstructured":"Ontario Ministry of Education: A Guide to Effective Literacy Instruction, Grades 4 to 6: A Multivolume Resource from the Ministry of Education. Volume one, Foundations of literacy instruction for the junior learner, p. 37. Ontario Ministry of Education (2006). https:\/\/books.google.com\/books?id=Y8F4oAEACAAJ"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Rayner, K., Pollatsek, A., Ashby, J., Clifton Jr, C.: Psychology of Reading. Psychology Press, London (2012)","DOI":"10.4324\/9780203155158"},{"issue":"3","key":"33_CR17","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1109\/TPAMI.2017.2707495","volume":"40","author":"EM Rudd","year":"2017","unstructured":"Rudd, E.M., Jain, L.P., Scheirer, W.J., Boult, T.E.: The extreme value machine. IEEE T-PAMI 40(3), 762\u2013768 (2017)","journal-title":"IEEE T-PAMI"},{"key":"33_CR18","unstructured":"Ruff, L., et al.: A unifying review of deep and shallow anomaly detection. arXiv preprint arXiv:2009.11732 (2020)"},{"issue":"3","key":"33_CR19","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O.: Imagenet large scale visual recognition challenge. IJCV 115(3), 211\u2013252 (2015)","journal-title":"IJCV"},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Sanchez, J.A., Romero, V., Toselli, A.H., Vidal, E.: ICFHR2016 competition on handwritten text recognition on the read dataset. In: ICFHR (2016)","DOI":"10.1109\/ICFHR.2016.0120"},{"issue":"11","key":"33_CR21","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1109\/TPAMI.2014.2321392","volume":"36","author":"WJ Scheirer","year":"2014","unstructured":"Scheirer, W.J., Jain, L.P., Boult, T.E.: Probability models for open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2317\u20132324 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"33_CR22","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","volume":"35","author":"WJ Scheirer","year":"2012","unstructured":"Scheirer, W.J., de Rezende Rocha, A., Sapkota, A., Boult, T.E.: Toward open set recognition. IEEE T-PAMI 35(7), 1757\u20131772 (2012)","journal-title":"IEEE T-PAMI"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE T-PAMI 39(11), 2298\u20132304 (2017)","DOI":"10.1109\/TPAMI.2016.2646371"},{"key":"33_CR24","unstructured":"Smith, D.A., Cordell, R.: A research agenda for historical and multilingual opticalcharacter recognition. Technical report, Northeastern University (2018)"},{"key":"33_CR25","doi-asserted-by":"publisher","unstructured":"Studer, L., et al.: A comprehensive study of imagenet pre-training for historical document image analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 720\u2013725 (September 2019). https:\/\/doi.org\/10.1109\/ICDAR.2019.00120. iSSN: 2379-2140","DOI":"10.1109\/ICDAR.2019.00120"},{"key":"33_CR26","doi-asserted-by":"publisher","unstructured":"Stutzmann, D.: Clustering of medieval scripts through computer image analysis: towards an evaluation protocol. Digit. Mediev. 10 (2016). https:\/\/doi.org\/10.16995\/dm.61. http:\/\/journal.digitalmedievalist.org\/\/articles\/10.16995\/dm.61\/. ISSN: 1715-0736","DOI":"10.16995\/dm.61"},{"key":"33_CR27","doi-asserted-by":"crossref","unstructured":"Wigington, C., Stewart, S., Davis, B., Barrett, B., Price, B., Cohen, S.: Data augmentation for recognition of handwritten words and lines using a CNN-LSTM network. In: ICDAR (2017)","DOI":"10.1109\/ICDAR.2017.110"},{"issue":"1","key":"33_CR28","first-page":"81","volume":"1","author":"RV Yampolskiy","year":"2008","unstructured":"Yampolskiy, R.V., Govindaraju, V.: Behavioural biometrics: a survey and classification. Int. J. Biom. 1(1), 81\u2013113 (2008)","journal-title":"Int. J. Biom."},{"issue":"8","key":"33_CR29","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1109\/TPAMI.2016.2613924","volume":"39","author":"H Zhang","year":"2016","unstructured":"Zhang, H., Patel, V.M.: Sparse representation-based open set recognition. IEEE T-PAMI 39(8), 1690\u20131696 (2016)","journal-title":"IEEE T-PAMI"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition \u2013 ICDAR 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86337-1_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T22:11:38Z","timestamp":1756851098000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86337-1_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863364","9783030863371"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86337-1_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"2 September 2021","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":"Lausanne","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2021","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":"icdar2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iapr.org\/icdar2021","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":"340","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":"182","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":"54% - 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":"2.9","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":"4.9","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":"Additionally, 13 competition reports are included.","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)"}}]}}