{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T02:03:14Z","timestamp":1778637794230,"version":"3.51.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031414978","type":"print"},{"value":"9783031414985","type":"electronic"}],"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\/978-3-031-41498-5_3","type":"book-chapter","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T19:23:19Z","timestamp":1692040999000},"page":"34-48","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["The Adaptability of\u00a0a\u00a0Transformer-Based OCR Model for\u00a0Historical Documents"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2063-5495","authenticated-orcid":false,"given":"Phillip Benjamin","family":"Str\u00f6bel","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2071-6407","authenticated-orcid":false,"given":"Tobias","family":"Hodel","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2922-051X","authenticated-orcid":false,"given":"Walter","family":"Boente","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2063-4516","authenticated-orcid":false,"given":"Martin","family":"Volk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,15]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Aradillas, J.C., Murillo-Fuentes, J.J., Olmos, P.M.: Boosting offline handwritten text recognition in historical documents with few labeled lines. IEEE Access 9, 76674\u201376688 (2021). https:\/\/ieeexplore.ieee.org\/document\/9438636","DOI":"10.1109\/ACCESS.2021.3082689"},{"key":"3_CR2","unstructured":"Bao, H., Dong, L., Wei, F.: BEiT: BERT pre-training of image transformers. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/pdf?id=p-BhZSz59o4"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Causer, T., Grint, K., Sichani, A.M., Terras, M.: \u2018Making such bargain\u2019: transcribe Bentham and the quality and cost-effectiveness of crowdsourced transcription. Digit. Sch. Hum. 33(3), 467\u2013487 (2018). https:\/\/doi-org.ezproxy.uzh.ch\/10.1093\/llc\/fqx064","DOI":"10.1093\/llc\/fqx064"},{"key":"3_CR4","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171\u20134186 (2019). https:\/\/doi.org\/10.48550\/arXiv.1810.04805","DOI":"10.48550\/arXiv.1810.04805"},{"key":"3_CR5","unstructured":"Dosovitskiy, A., et al.: An image is worth $$16 \\times 16$$ words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193\u2013202 (1980). https:\/\/doi.org\/10.1007\/BF00344251","DOI":"10.1007\/BF00344251"},{"key":"3_CR7","unstructured":"G\u00e4bler, U., et al. (eds.): Heinrich Bullinger Briefwechsel. Heinrich Bullinger Werke, Theologischer Verlag Z\u00fcrich (1974\u20132020)"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Granell, E., Chammas, E., Likforman-Sulem, L., Mart\u00ednez-Hinarejos, C.D., Mokbel, C., Cirstea, B.I.: Transcription of Spanish historical handwritten documents with deep neural networks. J. Imaging 4, 15 (2018). https:\/\/doi.org\/10.3390\/jimaging4010015","DOI":"10.3390\/jimaging4010015"},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1007\/978-3-642-24797-2_4","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"3_CR10","doi-asserted-by":"publisher","unstructured":"Hodel, T.M., Schoch, D.S., Schneider, C., Purcell, J.: General models for handwritten text recognition: feasibility and state-of-the art. German Kurrent as an example. J. Open Hum. Data 7(13), 1\u201310 (2021). https:\/\/doi.org\/10.5334\/johd.46","DOI":"10.5334\/johd.46"},{"issue":"4","key":"3_CR11","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989). https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput."},{"key":"3_CR12","doi-asserted-by":"publisher","unstructured":"Li, M., et al.: TrOCR: transformer-based optical character recognition with pre-trained models. arXiv (2021). https:\/\/doi.org\/10.48550\/arXiv.2109.10282","DOI":"10.48550\/arXiv.2109.10282"},{"key":"3_CR13","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1007\/978-3-030-19823-7_30","volume-title":"Artificial Intelligence Applications and Innovations","author":"J Mart\u00ednek","year":"2019","unstructured":"Mart\u00ednek, J., Lenc, L., Kr\u00e1l, P.: Training strategies for OCR systems for historical documents. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2019. IAICT, vol. 559, pp. 362\u2013373. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-19823-7_30"},{"key":"3_CR14","unstructured":"M\u00fchlberger, G., Seaward, L., Terras, M., et al.: Transforming scholarship in the archives through handwritten text recognition: Transkribus as a case study. J. Documentation 75(5), 954\u2013976 (2019). https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/JD-07-2018-0114\/full\/html"},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"Neudecker, C., et al.: OCR-D: an end-to-end open source OCR framework for historical printed documents. In: Proceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage, pp. 53\u201358 (2019). https:\/\/doi.org\/10.1145\/3322905.3322917","DOI":"10.1145\/3322905.3322917"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Ruder, S., Peters, M.E., Swayamdipta, S., Wolf, T.: Transfer learning in natural language processing. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, pp. 15\u201318 (2019). https:\/\/aclanthology.org\/N19-5004\/","DOI":"10.18653\/v1\/N19-5004"},{"issue":"11","key":"3_CR17","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997). https:\/\/doi.org\/10.1109\/78.650093","journal-title":"IEEE Trans. Signal Process."},{"key":"3_CR18","unstructured":"Shazeer, N., Stern, M.: Adafactor: Adaptive learning rates with sublinear memory cost. In: International Conference on Machine Learning, pp. 4596\u20134604 (2018). http:\/\/proceedings.mlr.press\/v80\/shazeer18a.html"},{"key":"3_CR19","doi-asserted-by":"publisher","unstructured":"Springmann, U., Fink, F., Schulz, K.U.: Automatic quality evaluation and (semi-)automatic improvement of OCR models for historical printings. arXiv (2016). https:\/\/doi.org\/10.48550\/arXiv.1606.05157","DOI":"10.48550\/arXiv.1606.05157"},{"key":"3_CR20","unstructured":"Springmann, U., L\u00fcdeling, A.: OCR of historical printings with an application to building diachronic corpora: A case study using the RIDGES herbal corpus. Digit. Hum. Q. 11(2) (2017). https:\/\/arxiv.org\/abs\/1608.02153"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Strau\u00df, T., Leifert, G., Labahn, R., Hodel, T., M\u00fchlberger, G.: ICFHR2018 competition on automated text recognition on a READ dataset. In: 16th International Conference on Frontiers in Handwriting Recognition, pp. 477\u2013482 (2018). https:\/\/ieeexplore.ieee.org\/document\/8583807","DOI":"10.1109\/ICFHR-2018.2018.00089"},{"key":"3_CR22","unstructured":"Str\u00f6bel, P.B., Clematide, S.: Improving OCR of black letter in historical newspapers: the unreasonable effectiveness of HTR models on low-resolution images. In: Proceedings of the Digital Humanities 2019 (2019). https:\/\/www.zora.uzh.ch\/id\/eprint\/177164\/"},{"key":"3_CR23","unstructured":"Terras, M.: The role of the library when computers can read. In: The Rise of AI: Implications and Applications of Artificial Intelligence in Academic Libraries, pp. 137\u2013148. ALAstore (2022). https:\/\/www.pure.ed.ac.uk\/ws\/portalfiles\/portal\/255303209\/Rise_of_AI_Chapter_11.pdf"},{"key":"3_CR24","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., J\u00e9gou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347\u201310357 (2021). https:\/\/proceedings.mlr.press\/v139\/touvron21a"},{"key":"3_CR25","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017). https:\/\/dl.acm.org\/doi\/10.5555\/3295222.3295349"},{"key":"3_CR26","unstructured":"Volk, M., et al.: Nunc profana tractemus. Detecting code-switching in a large corpus of 16th century letters. In: Proceedings of the 13th Language Resources and Evaluation Conference, pp. 2901\u20132908 (2022). https:\/\/aclanthology.org\/2022.lrec-1.311\/"},{"key":"3_CR27","unstructured":"Weidemann, M., Michael, J., Gr\u00fcning, T., Labahn, R.: HTR engine based on NNs P2 building deep architectures with TensorFlow. Technical report (2018). https:\/\/read.transkribus.eu\/wp-content\/uploads\/2018\/12\/D7.9_HTR_NN_final.pdf"},{"issue":"1","key":"3_CR28","doi-asserted-by":"publisher","first-page":"79","DOI":"10.21248\/jlcl.33.2018.219","volume":"33","author":"C Wick","year":"2018","unstructured":"Wick, C., Reul, C., Puppe, F.: Comparison of OCR accuracy on early printed books using the open source engines Calamari and OCRopus. Spec. Issue Autom. Text Layout Recogn. 33(1), 79\u201396 (2018). https:\/\/doi.org\/10.21248\/jlcl.33.2018.219","journal-title":"Spec. Issue Autom. Text Layout Recogn."},{"key":"3_CR29","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: 14th IAPR International Conference on Document Analysis and Recognition, pp. 639\u2013645 (2017). https:\/\/ieeexplore.ieee.org\/document\/8270041","DOI":"10.1109\/ICDAR.2017.110"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition \u2013 ICDAR 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41498-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T19:26:23Z","timestamp":1692041183000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41498-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031414978","9783031414985"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41498-5_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 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)"}}]}}