{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:46:20Z","timestamp":1772905580689,"version":"3.50.1"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031216473","type":"print"},{"value":"9783031216480","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21648-0_27","type":"book-chapter","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:05:14Z","timestamp":1669334714000},"page":"389-403","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Self-attention Networks for\u00a0Non-recurrent Handwritten Text Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2226-2672","authenticated-orcid":false,"given":"Rafael","family":"d\u2019Arce","sequence":"first","affiliation":[]},{"given":"Terence","family":"Norton","sequence":"additional","affiliation":[]},{"given":"Sion","family":"Hannuna","sequence":"additional","affiliation":[]},{"given":"Nello","family":"Cristianini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"27_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. CoRR abs\/1409.0473 (2015)"},{"key":"27_CR2","unstructured":"Barrere, K., Soullard, Y., Lemaitre, A., Co\u00fcasnon, B.: Transformers for historical handwritten text recognition. In: Doctoral Consortium - ICDAR 2021. Nibal Nayef and Jean-Christophe Burie, Lausanne, Switzerland (2021). https:\/\/hal.archives-ouvertes.fr\/hal-03485262"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Bledsoe, W.W., Browning, I.: Pattern recognition and reading by machine. In: Papers Presented at the 1\u20133 December 1959, Eastern Joint IRE-AIEE-ACM Computer Conference, pp. 225\u2013232. IRE-AIEE-ACM 1959 (Eastern), Association for Computing Machinery (1959)","DOI":"10.1145\/1460299.1460326"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., G\u00fcl\u00e7ehre, \u00c7., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. CoRR abs\/1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"27_CR5","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs\/1810.04805 (2018)"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Dong, L., Xu, S., Xu, B.: Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5884\u20135888 (2018)","DOI":"10.1109\/ICASSP.2018.8462506"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Fischer, A., Frinken, V., Forn\u00e9s, A., Bunke, H.: Transcription alignment of Latin manuscripts using hidden Markov models. In: Proceedings of the 2011 Workshop on Historical Document Imaging and Processing, pp. 29\u201336. Association for Computing Machinery (2011)","DOI":"10.1145\/2037342.2037348"},{"issue":"7","key":"27_CR8","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1016\/j.patrec.2011.09.009","volume":"33","author":"A Fischer","year":"2012","unstructured":"Fischer, A., Keller, A., Frinken, V., Bunke, H.: Lexicon-free handwritten word spotting using character HMMs. Pattern Recogn. Lett. 33(7), 934\u2013942 (2012)","journal-title":"Pattern Recogn. Lett."},{"issue":"21","key":"27_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app10217711","volume":"10","author":"AFS Neto","year":"2020","unstructured":"Neto, A.F.S., Bezerra, B.L.D., Toselli, A.H.: Towards the natural language processing as spelling correction for offline handwritten text recognition systems. Appl. Sci. 10(21), 1\u201329 (2020). https:\/\/doi.org\/10.3390\/app10217711","journal-title":"Appl. Sci."},{"key":"27_CR10","unstructured":"Neto, A.F.S., Bezerra, B.L.D., Toselli, A.H., Lima, E.B.: HTR-Flor: a deep learning System for Offline Handwritten Text Recognition. In: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 54\u201361 (2020)"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Gatos, B., et al.: Ground-truth production in the transcriptorium project. In: 2014 11th IAPR International Workshop on Document Analysis Systems, pp. 237\u2013241 (2014)","DOI":"10.1109\/DAS.2014.23"},{"key":"27_CR12","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256. JMLR Workshop and Conference Proceedings (2010)"},{"key":"27_CR13","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-642-24797-2_2","volume-title":"Supervised Sequence Labelling with Recurrent Neural Networks","author":"A Graves","year":"2012","unstructured":"Graves, A.: Supervised sequence labelling. In: Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence, vol. 385, pp. 5\u201313. Springer, Berlin (2012). https:\/\/doi.org\/10.1007\/978-3-642-24797-2_2"},{"key":"27_CR14","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: Proceedings of the 23rd International Conference on Machine Learning, pp. 369\u2013376. ICML 2006, Association for Computing Machinery, New York, NY, USA (2006)","DOI":"10.1145\/1143844.1143891"},{"key":"27_CR15","unstructured":"Graves, A., Fern\u00e1ndez, S., Liwicki, M., Bunke, H., Schmidhuber, J.: Unconstrained online handwriting recognition with recurrent neural networks. In: Proceedings of the 20th International Conference on Neural Information Processing Systems, pp. 577\u2013584. NIPS 2007 (2007)"},{"issue":"2","key":"27_CR16","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1093\/comjnl\/4.2.129","volume":"4","author":"RL Grimsdale","year":"1961","unstructured":"Grimsdale, R.L., Bullingham, J.M.: Character recognition by digital computer using a special flying-spot scanner. Comput. J. 4(2), 129\u2013136 (1961)","journal-title":"Comput. J."},{"key":"27_CR17","unstructured":"Hinton, G., Srivastava, N., Swersky, K.: Neural networks for machine learning lecture 6a overview of mini-batch gradient descent (2012)"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Hwang, K., Sung, W.: Character-level incremental speech recognition with recurrent neural networks. CoRR abs\/1601.06581 (2016)","DOI":"10.1109\/ICASSP.2016.7472696"},{"key":"27_CR19","unstructured":"Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Synthetic data and artificial neural networks for natural scene text recognition. CoRR abs\/1406.2227 (2014)"},{"key":"27_CR20","unstructured":"Kang, L., Riba, P., Rusi\u00f1ol, M., Forn\u00e9s, A., Villegas, M.: Pay attention to what you read: non-recurrent handwritten text-line recognition (2020)"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Krishnan, P., Jawahar, C.V.: HWNet v2: an efficient word image representation for handwritten documents. CoRR abs\/1802.06194 (2018)","DOI":"10.1007\/s10032-019-00336-x"},{"issue":"6","key":"27_CR22","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"key":"27_CR23","unstructured":"LeCun, Y. et al.: Handwritten digit recognition with a back-propagation network. In: Touretzky, D. (ed.) Advances in Neural Information Processing Systems. vol. 2. Morgan-Kaufmann (1990)"},{"issue":"4","key":"27_CR24","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)","journal-title":"Neural Comput."},{"key":"27_CR25","unstructured":"Lipton, Z.C.: A critical review of recurrent neural networks for sequence learning. CoRR abs\/1506.00019 (2015)"},{"key":"27_CR26","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. CoRR abs\/1907.11692 (2019)"},{"key":"27_CR27","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s100320200071","volume":"5","author":"U Marti","year":"2002","unstructured":"Marti, U., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition. Int. J. Doc. Anal. Recogn. 5, 39\u201346 (2002)","journal-title":"Int. J. Doc. Anal. Recogn."},{"key":"27_CR28","unstructured":"Michael, J., Weidemann, M., Labahn, R.: D7.9 HTR engine based on NNs P3(2022). https:\/\/readcoop.eu\/wp-content\/uploads\/2018\/12\/Del_D7_9.pdf"},{"key":"27_CR29","doi-asserted-by":"crossref","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)","DOI":"10.1109\/ICDAR.2017.20"},{"key":"27_CR30","unstructured":"PyLaia (2022). https:\/\/github.com\/jpuigcerver\/PyLaia. Accessed 10 July 2022"},{"key":"27_CR31","unstructured":"Handwritten text recognition (HTR) using TensorFlow 2.x (2022). https:\/\/github.com\/arthurflor23\/handwritten-text-recognition. Accessed 10 July 2022"},{"issue":"8","key":"27_CR32","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"key":"27_CR33","unstructured":"Ramesh, A., et al.: Zero-shot text-to-image generation. CoRR abs\/2102.12092 (2021)"},{"key":"27_CR34","doi-asserted-by":"crossref","unstructured":"Salazar, J., Kirchhoff, K., Huang, Z.: Self-attention networks for connectionist temporal classification in speech recognition. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)","DOI":"10.1109\/ICASSP.2019.8682539"},{"issue":"5","key":"27_CR35","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1108\/JD-07-2018-0114","volume":"75","author":"L Seaward","year":"2019","unstructured":"Seaward, L., et al.: Transforming scholarship in the archives through handwritten text recognition: transkribus as a case study. J. Documentation 75(5), 954\u2013976 (2019)","journal-title":"J. Documentation"},{"key":"27_CR36","unstructured":"Simard, P., Steinkraus, D., Platt, J.: Best practices for convolutional neural networks applied to visual document analysis. In: Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings, pp. 958\u2013963 (2003)"},{"key":"27_CR37","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. CoRR abs\/1409.3215 (2014)"},{"key":"27_CR38","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez, J.A., Romero, V., Toselli, A.H., Vidal, E.: ICFHR2014 competition on handwritten text recognition on transcriptorium datasets (HTRtS). In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 785\u2013790 (2014)","DOI":"10.1109\/ICFHR.2014.137"},{"key":"27_CR39","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.patcog.2019.05.025","volume":"94","author":"JA S\u00e1nchez","year":"2019","unstructured":"S\u00e1nchez, J.A., Romero, V., Toselli, A.H., Villegas, M., Vidal, E.: A set of benchmarks for handwritten text recognition on historical documents. Pattern Recogn. 94, 122\u2013134 (2019)","journal-title":"Pattern Recogn."},{"key":"27_CR40","unstructured":"Text Recognition Data Generator (2022). https:\/\/github.com\/Belval\/TextRecognitionDataGenerator. Accessed 10 July 2022"},{"key":"27_CR41","unstructured":"The Brown Corpus (2022). https:\/\/www.nltk.org\/book\/ch02.html#brown-corpus. Accessed 10 July 2022"},{"key":"27_CR42","unstructured":"Transkribus Glossary HTR+ (2022). https:\/\/readcoop.eu\/glossary\/htr-plus\/. Accessed 10 July 2022"},{"key":"27_CR43","unstructured":"Transkribus Glossary PyLaia (2022). https:\/\/readcoop.eu\/glossary\/pylaia\/. Accessed 10 July 2022"},{"key":"27_CR44","unstructured":"Vaswani, A., et al.: Attention is all you need. CoRR abs\/1706.03762 (2017)"},{"key":"27_CR45","unstructured":"Weidemann, M., Michael, J., Gr\u00fcning, T., Labahn, R.: D7.9 HTR engine based on NNs P3 (2022). https:\/\/readcoop.eu\/wp-content\/uploads\/2017\/12\/Del_D7_8.pdf"},{"key":"27_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/978-3-030-86334-0_8","volume-title":"Document Analysis and Recognition \u2013 ICDAR 2021","author":"C Wick","year":"2021","unstructured":"Wick, C., Z\u00f6llner, J., Gr\u00fcning, T.: Transformer for handwritten text recognition using bidirectional post-decoding. In: Llad\u00f3s, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12823, pp. 112\u2013126. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86334-0_8"},{"key":"27_CR47","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: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, pp. 639\u2013645 (2017)","DOI":"10.1109\/ICDAR.2017.110"},{"key":"27_CR48","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J.G., Salakhutdinov, R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. CoRR abs\/1906.08237 (2019)"}],"container-title":["Lecture Notes in Computer Science","Frontiers in Handwriting Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21648-0_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:51:56Z","timestamp":1710341516000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21648-0_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031216473","9783031216480"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21648-0_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"25 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICFHR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Frontiers in Handwriting Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hyderabad","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icfhr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icfhr2022.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":"61","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":"36","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":"1","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":"59% - 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":"4","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)"}}]}}