{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:30:57Z","timestamp":1767339057706,"version":"3.40.5"},"reference-count":113,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Fraunhofer-Institut f\u00fcr Integrierte Schaltungen IIS"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["IJDAR"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Handwriting is one of the most frequently occurring patterns in everyday life and with it comes challenging applications such as handwriting recognition, writer identification and signature verification. In contrast to offline HWR that only uses spatial information (i.e., images), online HWR uses richer spatio-temporal information (i.e., trajectory data or inertial data). While there exist many offline HWR datasets, there are only little data available for the development of OnHWR methods on paper as it requires hardware-integrated pens. This paper presents data and benchmark models for real-time sequence-to-sequence learning and single character-based recognition. Our data are recorded by a sensor-enhanced ballpoint pen, yielding sensor data streams from triaxial accelerometers, a gyroscope, a magnetometer and a force sensor at 100\u00a0Hz. We propose a variety of datasets including equations and words for both the writer-dependent and writer-independent tasks. Our datasets allow a comparison between classical OnHWR on tablets and on paper with sensor-enhanced pens. We provide an evaluation benchmark for seq2seq and single character-based HWR using recurrent and temporal convolutional networks and transformers combined with a connectionist temporal classification (CTC) loss and cross-entropy (CE) losses. Our convolutional network combined with BiLSTMs outperforms transformer-based architectures, is on par with InceptionTime for sequence-based classification tasks and yields better results compared to 28 state-of-the-art techniques. Time-series augmentation methods improve the sequence-based task, and we show that CE variants can improve the single classification task. Our implementations together with the large benchmark of state-of-the-art techniques of novel OnHWR datasets serve as a baseline for future research in the area of OnHWR on paper.<\/jats:p>","DOI":"10.1007\/s10032-022-00415-6","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T19:02:42Z","timestamp":1663873362000},"page":"385-414","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4392-0830","authenticated-orcid":false,"given":"Felix","family":"Ott","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8772-9202","authenticated-orcid":false,"given":"David","family":"R\u00fcgamer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6670-3698","authenticated-orcid":false,"given":"Lucas","family":"Heublein","sequence":"additional","affiliation":[]},{"given":"Tim","family":"Hamann","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3967-9578","authenticated-orcid":false,"given":"Jens","family":"Barth","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6002-6980","authenticated-orcid":false,"given":"Bernd","family":"Bischl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8108-0230","authenticated-orcid":false,"given":"Christopher","family":"Mutschler","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"key":"415_CR1","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/ICDAR.2011.289","volume":"4","author":"HE Abed","year":"2010","unstructured":"Abed, H.E., Kherallah, M., M\u00e4rgner, V., Alimi, A.M.: On-line Arabic handwriting recognition competition: ADAB database and participating systems. IJDAR 4, 15\u201323 (2010). https:\/\/doi.org\/10.1109\/ICDAR.2011.289","journal-title":"IJDAR"},{"key":"415_CR2","doi-asserted-by":"publisher","unstructured":"Alimoglu, F., Alpaydin, E.: Combining multiple representations and classifiers for pen-based handwritten digit recognition. In: ICDAR, vol.\u00a02. Ulm, Germany (1997). https:\/\/doi.org\/10.1109\/ICDAR.1997.620583","DOI":"10.1109\/ICDAR.1997.620583"},{"issue":"12","key":"415_CR3","doi-asserted-by":"publisher","first-page":"2552","DOI":"10.1109\/TPAMI.2014.2339814","volume":"36","author":"J Almaz\u00e1n","year":"2014","unstructured":"Almaz\u00e1n, J., Gordo, A., Forn\u00e9s, A., Valveny, E.: Word spotting and recognition with embedded attributes. TPAMI 36(12), 2552\u20132566 (2014). https:\/\/doi.org\/10.1109\/TPAMI.2014.2339814","journal-title":"TPAMI"},{"key":"415_CR4","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.sbspro.2015.01.470","volume":"176","author":"MAP Alonso","year":"2015","unstructured":"Alonso, M.A.P.: Metacognition and sensorimotor components underlying the process of handwriting and keyboarding and their impact on learning. An analysis from the perspective of embodied psychology. Procedia Soc. Behav. Sci. 176, 263\u2013269 (2015). https:\/\/doi.org\/10.1016\/j.sbspro.2015.01.470","journal-title":"Procedia Soc. Behav. Sci."},{"key":"415_CR5","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. In: arXiv:1607.06450 (2016)"},{"key":"415_CR6","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. In: arXiv:1803.01271 (2018)"},{"key":"415_CR7","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.buildenv.2015.02.013","volume":"89","author":"P Barrett","year":"2015","unstructured":"Barrett, P., Davies, F., Zhang, Y., Barrett, L.: The impact of classroom design on pupils\u2019 learning: final results of a holistic. Multi-level analysis. Build. Environ. 89, 118\u2013133 (2015). https:\/\/doi.org\/10.1016\/j.buildenv.2015.02.013","journal-title":"Build. Environ."},{"issue":"11","key":"415_CR8","doi-asserted-by":"publisher","first-page":"3452","DOI":"10.1016\/j.patcog.2008.04.003","volume":"41","author":"R Bertolami","year":"2008","unstructured":"Bertolami, R., Bunke, H.: Hidden Markov model-based ensemble methods for offline handwritten text line recognition. Pattern Recogn. 41(11), 3452\u20133460 (2008). https:\/\/doi.org\/10.1016\/j.patcog.2008.04.003","journal-title":"Pattern Recogn."},{"key":"415_CR9","unstructured":"Bluche, T.: Deep neural networks for large vocabulary handwritten text recognition. Dissertation (2015)"},{"key":"415_CR10","unstructured":"Bluche, T.: Joint line segmentation and transcription for end-to-end handwritten paragraph recognition. In: NIPS, pp. 838\u2014846. Barcelona, Spain (2016)"},{"issue":"4","key":"415_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3494956","volume":"5","author":"Y Bu","year":"2021","unstructured":"Bu, Y., Xie, L., Ying, Y., Ning, C.W.J., Cao, J., Lu, S.: Handwriting-assistant: reconstructing continuous strokes with millimeter-level accuracy via attachable inertial sensors. IMWUT 5(4), 1\u201325 (2021). https:\/\/doi.org\/10.1145\/3494956","journal-title":"IMWUT"},{"key":"415_CR12","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s10032-020-00350-4","volume":"23","author":"V Carbune","year":"2020","unstructured":"Carbune, V., Gonnet, P., Deselaers, T., Rowley, H.A., Daryin, A., Calvo, M., Wang, L.L., Keysers, D., Feuz, S., Gervais, P.: Fast Multi-language LSTM-based online handwriting recognition. IJDAR 23, 89\u2013102 (2020). https:\/\/doi.org\/10.1007\/s10032-020-00350-4","journal-title":"IJDAR"},{"key":"415_CR13","unstructured":"Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J., Mohiuddin, A., Kaiser, L., Belanger, D., Colwell, L., Weller, A.: Rethinking Attention with Performers. In: ICLR (2021)"},{"key":"415_CR14","unstructured":"Chowdhury, A., Vig, L.: An efficient end-to-end neural model for handwritten text recognition. In: BMVC (2018)"},{"key":"415_CR15","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: arXiv:1412.3555 (2014)"},{"issue":"3","key":"415_CR16","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1145\/363958.363994","volume":"7","author":"FJ Damerau","year":"1964","unstructured":"Damerau, F.J.: A technique for computer detection and correction of spelling errors. Commun. ACM 7(3), 171\u2013176 (1964). https:\/\/doi.org\/10.1145\/363958.363994","journal-title":"Commun. ACM"},{"issue":"2","key":"415_CR17","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/THMS.2014.2365723","volume":"45","author":"T Deselaers","year":"2015","unstructured":"Deselaers, T., Keysers, D., Hosang, J., Rowley, H.A.: GyroPen: gyroscopes for pen-input with mobile phones. THMS 45(2), 263\u2013271 (2015). https:\/\/doi.org\/10.1109\/THMS.2014.2365723","journal-title":"THMS"},{"key":"415_CR18","doi-asserted-by":"publisher","unstructured":"Doetsch, P., Kozielski, M., Ney, H.: Fast and robust training of recurrent neural networks for offline handwriting recognition. In: ICFHR, pp. 279\u2013284 (2014). https:\/\/doi.org\/10.1109\/ICFHR.2014.54","DOI":"10.1109\/ICFHR.2014.54"},{"key":"415_CR19","doi-asserted-by":"publisher","unstructured":"Dreuw, P., Doetsch, P., Plahl, C., Ney, H.: Hierarchical hybrid MLP\/HMM or rather MLP Ffatures for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition. In: ICIP, pp. 3541\u20133544 (2011). https:\/\/doi.org\/10.1109\/ICIP.2011.6116480","DOI":"10.1109\/ICIP.2011.6116480"},{"key":"415_CR20","doi-asserted-by":"publisher","unstructured":"Dutta, K., Krishnan, P., Mathew, M., Jawahar, C.V.: Improving CNN-RNN hybrid networks for handwriting recognition. In: ICFHR, pp. 80\u201385 (2018). https:\/\/doi.org\/10.1109\/ICFHR-2018.2018.00023","DOI":"10.1109\/ICFHR-2018.2018.00023"},{"key":"415_CR21","doi-asserted-by":"crossref","unstructured":"Elsayed, N., Maida, A.S., Bayoumi, M.: Deep gated recurrent and convolutional network hybrid model for univariate time series classification. In: arXiv:1812.07683 (2018)","DOI":"10.14569\/IJACSA.2019.0100582"},{"issue":"4","key":"415_CR22","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1109\/TPAMI.2010.141","volume":"33","author":"S Espa\u00f1a-Boquera","year":"2010","unstructured":"Espa\u00f1a-Boquera, S., Castro-Bleda, M.J., Gorbe-Moya, J., Zamora-Martinez, F.: Improving Offline handwritten text recognition with hybrid HMM\/ANN models. TPAMI 33(4), 767\u2013779 (2010). https:\/\/doi.org\/10.1109\/TPAMI.2010.141","journal-title":"TPAMI"},{"issue":"1","key":"415_CR23","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.asej.2010.09.007","volume":"1","author":"MMM Fahmy","year":"2010","unstructured":"Fahmy, M.M.M.: Online signature verification and handwriting classification. ASEJ 1(1), 59\u201370 (2010). https:\/\/doi.org\/10.1016\/j.asej.2010.09.007","journal-title":"ASEJ"},{"key":"415_CR24","doi-asserted-by":"crossref","unstructured":"Fauvel, K., \u00c9lisa Fromont, Masson, V., Faverdin, P., Termier, A.: XEM: An explainable ensemble method for multivariate time series classification. In: arXiv:2005.03645 (2020)","DOI":"10.3390\/math9233137"},{"key":"415_CR25","unstructured":"Fawaz, H.I., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D.F., Weberf, J., Webb, G.I., Idoumghar, L., Muller, P.A., Petitjean, F.: InceptionTime: finding AlexNet for Time series classification. In: arXiv:1909.04939 (2019)"},{"key":"415_CR26","doi-asserted-by":"publisher","unstructured":"Fogel, S., Averbuch-Elor, H., Cohen, S., Mazor, S., Litman, R.: ScrabbleGAN: semi-supervised varying lenght handwritten text generation. In: CVPR, pp. 4324\u20134333 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00438","DOI":"10.1109\/CVPR42600.2020.00438"},{"key":"415_CR27","doi-asserted-by":"publisher","unstructured":"Frinken, V., Uchida, S.: Deep BLSTM neural networks for unconstrained continuous handwritten text recognition. In: ICDAR, pp. 911\u2013915 (2015). https:\/\/doi.org\/10.1109\/ICDAR.2015.7333894","DOI":"10.1109\/ICDAR.2015.7333894"},{"key":"415_CR28","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2016.01308","author":"S Gerth","year":"2016","unstructured":"Gerth, S., Klassert, A., Dolk, T., Fliesser, M., Fischer, M.H., Nottbusch, G., Festman, J.: Is handwriting performance affected by the writing surface? Comparing preschoolers\u2019, Second Graders\u2019, and adults\u2019 Writing Performance on a Tablet vs Paper. Front. Psychol. (2016). https:\/\/doi.org\/10.3389\/fpsyg.2016.01308","journal-title":"Front. Psychol."},{"key":"415_CR29","unstructured":"Graves, A.: Generating sequences with recurrent neural networks. In: arXiv:1308.0850 (2014)"},{"key":"415_CR30","doi-asserted-by":"publisher","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: ICML, pp. 369\u2013376. Pittsburgh, PA (2006). https:\/\/doi.org\/10.1145\/1143844.1143891","DOI":"10.1145\/1143844.1143891"},{"issue":"5","key":"415_CR31","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TPAMI.2008.137","volume":"31","author":"A Graves","year":"2009","unstructured":"Graves, A., Liwicki, M., Fern\u00e1ndez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. TPAMI 31(5), 855\u2013868 (2009). https:\/\/doi.org\/10.1109\/TPAMI.2008.137","journal-title":"TPAMI"},{"key":"415_CR32","unstructured":"Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. In: NIPS, pp. 545\u2013552 (2008)"},{"key":"415_CR33","doi-asserted-by":"publisher","unstructured":"Guyon, I., Schomaker, L., Plamondon, R., Liberman, M., Janet, S.: UNIPEN project of on-line data exchange and recognizer benchmarks. In: ICPR, vol.\u00a03 (1994). https:\/\/doi.org\/10.1109\/ICPR.1994.576870","DOI":"10.1109\/ICPR.1994.576870"},{"key":"415_CR34","doi-asserted-by":"publisher","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: CVPR, pp. 558\u2013567. Long Beach, CA (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00065","DOI":"10.1109\/CVPR.2019.00065"},{"key":"415_CR35","doi-asserted-by":"publisher","DOI":"10.1186\/s13640-015-0102-5","author":"R Hussain","year":"2015","unstructured":"Hussain, R., Raza, A., Siddiqi, I., Khurshid, K., Djeddi, C.: A comprehensive survey of handwritten document benchmarks: Structure, usage and evaluation. J. Image Video Process. (2015). https:\/\/doi.org\/10.1186\/s13640-015-0102-5","journal-title":"J. Image Video Process."},{"key":"415_CR36","unstructured":"Jaegle, A., Gimeno, F., Brock, A., Zisserman, A., Vinyals, O., Carreira, J.: Perceiver: general perception with iterative attention. In: ICML (2021)"},{"key":"415_CR37","doi-asserted-by":"publisher","first-page":"9772","DOI":"10.1007\/s11227-020-03222-0","volume":"76","author":"M Kaity","year":"2020","unstructured":"Kaity, M., Balakrishnan, V.: An integrated semi-automated framework for domain-based polarity words extraction from an unannotated non-English corpus. J. Supercomput. 76, 9772\u20139799 (2020). https:\/\/doi.org\/10.1007\/s11227-020-03222-0","journal-title":"J. Supercomput."},{"key":"415_CR38","unstructured":"Kang, L., Riba, P., Rusinol, M., Fornes, A., Villegas, M.: Pay attention to what you read: non-recurrent handwritten text-line recognition. In: arXiv:2005.13044 (2020)"},{"key":"415_CR39","doi-asserted-by":"crossref","unstructured":"Karim, F., Majumdar, S., Darabi, H., Chen, S.: LSTM fully convolutional networks for time series classification. In: arXiv:1709.05206 (2017)","DOI":"10.1109\/ACCESS.2017.2779939"},{"key":"415_CR40","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neunet.2019.04.014","volume":"116","author":"F Karim","year":"2019","unstructured":"Karim, F., Majumdar, S., Darabi, H., Harford, S.: Multivariate LSTM-FCNs for time series classification. Neural Netw. 116, 237\u2013245 (2019). https:\/\/doi.org\/10.1016\/j.neunet.2019.04.014","journal-title":"Neural Netw."},{"issue":"6","key":"415_CR41","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1109\/TPAMI.2016.2572693","volume":"36","author":"D Keysers","year":"2017","unstructured":"Keysers, D., Deselaers, T., Rowley, H.A., Wang, L.L., Carbune, V.: Multi-language online handwriting recognition. TPAMI 36(6), 1180\u20131194 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2572693","journal-title":"TPAMI"},{"key":"415_CR42","unstructured":"Kherallah, M., Elbaati, A., Abed, H.E., Alimi, A.M.: The On\/Off (LMCA) Dual Arabic handwriting database. In: ICFHR (2008)"},{"key":"415_CR43","doi-asserted-by":"crossref","unstructured":"Kim, S., Hori, T., Watanabe, S.: Joint CTC-attention based end-to-end speech recognition using multi-task learning. In: arXiv:1609.06773 (2017)","DOI":"10.1109\/ICASSP.2017.7953075"},{"key":"415_CR44","unstructured":"Kitaev, N., Kaiser, L., Levskaya, A.: Reformer: the efficient transformer. In: ICLR (2020)"},{"key":"415_CR45","unstructured":"Kla\u00df, A., Lorenz, S.M., Lauer-Schmaltz, M.W., R\u00fcgamer, D., Bischl, B., Mutschler, C., Ott, F.: Uncertainty-aware evaluation of time-series classification for online handwriting recognition with domain shift. In: IJCAI-ECAI Workshop on Spatio-Temporal Reasoning and Learning (STRL), vol. 3190. Vienna, Austria (2022)"},{"key":"415_CR46","doi-asserted-by":"publisher","unstructured":"Koellner, C., Kurz, M., Sonnleitner, E.: What did you mean? An evaluation of online character recognition approaches. In: WiMob, pp. 1\u20136. Barcelona, Spain (2019). https:\/\/doi.org\/10.1109\/WiMOB.2019.8923384","DOI":"10.1109\/WiMOB.2019.8923384"},{"key":"415_CR47","doi-asserted-by":"publisher","unstructured":"Kowsari, K., Meimandi, K.J., Heidarysafa, M., Mendu, S., Barnes, L., Brown, D.: Text classification algorithms: a survey. In: Information, vol. 10(4). Switzerland (2019). https:\/\/doi.org\/10.3390\/info10040150","DOI":"10.3390\/info10040150"},{"issue":"4","key":"415_CR48","first-page":"845","volume":"163","author":"WI Lewenstein","year":"1965","unstructured":"Lewenstein, W.I.: Binary codes capable of correcting deletions, insertions, and reversals. Dokl. Akad. Nauk. SSSR 163(4), 845\u2013848 (1965)","journal-title":"Dokl. Akad. Nauk. SSSR"},{"key":"415_CR49","unstructured":"Li, Q., Peng, H., Li, J., Xia, C., Yang, R., Sun, L., Yu, P.S., He, L.: A survey on text classification: from shallow to deep learning. In: arXiv:arXiv:2008.00364 (2020)"},{"key":"415_CR50","doi-asserted-by":"publisher","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV, pp. 2980\u20132988 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.324","DOI":"10.1109\/ICCV.2017.324"},{"key":"415_CR51","doi-asserted-by":"publisher","unstructured":"Liwicki, M., Bunke, H.: IAM-OnDB - an On-Line English sentence database acquired from handwritten text on a whiteboard. In: ICDAR, pp. 956\u2013961. Seoul, Korea (2005). https:\/\/doi.org\/10.1109\/ICDAR.2005.132","DOI":"10.1109\/ICDAR.2005.132"},{"issue":"1","key":"415_CR52","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.patcog.2008.10.030","volume":"22","author":"M Liwicki","year":"2011","unstructured":"Liwicki, M., Bunke, H., Pittman, J.A., Knerr, S.: Combining diverse systems for handwritten text line recognition. Mach. Vis. Appl. 22(1), 39\u201351 (2011). https:\/\/doi.org\/10.1016\/j.patcog.2008.10.030","journal-title":"Mach. Vis. Appl."},{"key":"415_CR53","doi-asserted-by":"publisher","unstructured":"Long Ma, L., dan Liu, H., Wu, J.: MRG-OHTC database for online handwritten Tibetan character recognition. In: ICDAR, pp. 207\u2013211. Beijing, China (2011). https:\/\/doi.org\/10.1109\/ICDAR.2011.50","DOI":"10.1109\/ICDAR.2011.50"},{"key":"415_CR54","doi-asserted-by":"publisher","unstructured":"Michael, J., Labahn, R., Gr\u00fcning, T., Z\u00f6llner, J.: Evaluating sequence-to-sequence models for handwritten text recognition. In: ICDAR (2019). https:\/\/doi.org\/10.1109\/ICDAR.2019.00208","DOI":"10.1109\/ICDAR.2019.00208"},{"key":"415_CR55","doi-asserted-by":"publisher","unstructured":"Mouch\u00e8re, H., Viard-Gaudin, C., Zanibbi, R., Garain, U., Kim, D.H., Kim, J.H.: ICDAR 2013 CROHME: third international competition on recognition of online handwritten mathematical expressions. In: ICDAR. Washington, DC (2013). https:\/\/doi.org\/10.1109\/ICDAR.2013.288","DOI":"10.1109\/ICDAR.2013.288"},{"key":"415_CR56","doi-asserted-by":"publisher","unstructured":"Nakagawa, M., Higashiyama, T., Yamanaka, Y., Sawada, S., Higashigawa, L., Akiyama, K.: On-line handwritten character pattern database sampled in a sequence of sentences without any writing instructions. In: ICDAR, vol.\u00a01, pp. 376\u2013381. Ulm, Germany (1997). https:\/\/doi.org\/10.1109\/ICDAR.1997.619874","DOI":"10.1109\/ICDAR.1997.619874"},{"key":"415_CR57","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10032-004-0125-4","volume":"7","author":"M Nakagawa","year":"2004","unstructured":"Nakagawa, M., Matsumoto, K.: Collection of on-line handwritten Japanese character pattern databases and their analysis. IJDAR 7, 69\u201381 (2004). https:\/\/doi.org\/10.1007\/s10032-004-0125-4","journal-title":"IJDAR"},{"key":"415_CR58","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.patcog.2018.01.013","volume":"78","author":"HT Nguyen","year":"2018","unstructured":"Nguyen, H.T., Nguyen, C.T., Bao, P.T., Nakagawa, M.: A database of unconstrained Vietnamese online handwriting and recognition experiments by recurrent neural networks. Pattern Recogn. 78, 291\u2013306 (2018). https:\/\/doi.org\/10.1016\/j.patcog.2018.01.013","journal-title":"Pattern Recogn."},{"key":"415_CR59","doi-asserted-by":"publisher","unstructured":"Nguyen, H.T., Nguyen, C.T., Nakagawa, M.: ICFHR 2018 - competition on vietnamese online handwritten text recognition using HANDS-VNOnDB (VOHTR2018). In: ICFHR, pp. 494\u2013499. Niagara Falls, NY (2018). https:\/\/doi.org\/10.1109\/ICFHR-2018.2018.00092","DOI":"10.1109\/ICFHR-2018.2018.00092"},{"key":"415_CR60","unstructured":"Ofitserov, E., Tsvetkov, V., Nazarov, V.: Soft edit distance for differentiable comparison of symbolic sequences. In: arXiv:1904.12562 (2019)"},{"key":"415_CR61","unstructured":"Oguiza, I.: tsai - a state-of-the-art deep learning library for time series and sequential data. Github (2020). https:\/\/github.com\/timeseriesAI\/tsai"},{"key":"415_CR62","doi-asserted-by":"crossref","unstructured":"Ott, F., R\u00fcgamer, D., Heublein, L., Bischl, B., Mutschler, C.: Cross-modal common representation learning with triplet loss functions. In: arXiv:2202.07901 (2022)","DOI":"10.31219\/osf.io\/pbzd7"},{"key":"415_CR63","doi-asserted-by":"publisher","unstructured":"Ott, F., R\u00fcgamer, D., Heublein, L., Bischl, B., Mutschler, C.: Domain adaptation for time-series classification to mitigate covariate shift. In: ACMMM (2022). https:\/\/doi.org\/10.1145\/3503161.3548167","DOI":"10.1145\/3503161.3548167"},{"key":"415_CR64","doi-asserted-by":"publisher","unstructured":"Ott, F., R\u00fcgamer, D., Heublein, L., Bischl, B., Mutschler, C.: Joint classification and trajectory regression of online handwriting using a multi-task learning approach. In: WACV, pp. 266\u2013276. Waikoloa, HI (2022). https:\/\/doi.org\/10.1109\/WACV51458.2022.00131","DOI":"10.1109\/WACV51458.2022.00131"},{"key":"415_CR65","doi-asserted-by":"publisher","unstructured":"Ott, F., Wehbi, M., Hamann, T., Barth, J., Eskofier, B., Mutschler, C.: The OnHW Dataset: Online Handwriting Recognition from IMU-enhanced ballpoint pens with machine learning. In: IMWUT, vol. 4(3), Article 92. Canc\u00fan, Mexico (2020). https:\/\/doi.org\/10.1145\/3411842","DOI":"10.1145\/3411842"},{"key":"415_CR66","doi-asserted-by":"publisher","unstructured":"Peng, D., Xie, C., Li, H., Jin, L., Xie, Z., Ding, K., Huang, Y., Wu, Y.: Towards fast, accurate and compact online handwritten Chinese text recognition. In: ICDAR, pp. 157\u2013171 (2021). https:\/\/doi.org\/10.1007\/978-3-030-86334-0_11","DOI":"10.1007\/978-3-030-86334-0_11"},{"key":"415_CR67","unstructured":"Pereyra, G., Tucker, G., Chorowski, J., Kaiser, \u0141., Hinton, G.: Regularizing neural networks by penalizing confident output distributions. In: ICLR Workshop (2017)"},{"key":"415_CR68","doi-asserted-by":"publisher","unstructured":"Pham, V., Bluche, T., Kermorvant, C., Louradour, J.: Dropout improves recurrent neural networks for handwriting recognition. In: ICFHR, pp. 285\u2013290 (2014). https:\/\/doi.org\/10.1109\/ICFHR.2014.55","DOI":"10.1109\/ICFHR.2014.55"},{"issue":"1","key":"415_CR69","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/34.824821","volume":"22","author":"R Plamondon","year":"2000","unstructured":"Plamondon, R., Srihari, S.N.: On-line and off-line handwriting recognition: a comprehensive survey. TPAMI 22(1), 63\u201384 (2000). https:\/\/doi.org\/10.1109\/34.824821","journal-title":"TPAMI"},{"key":"415_CR70","doi-asserted-by":"publisher","unstructured":"Puigcerver, J.: Are multidimensional recurrent layers really necessary for handwritten text recognition?. In: ICDAR, pp. 67\u201372 (2017). https:\/\/doi.org\/10.1109\/ICDAR.2017.20","DOI":"10.1109\/ICDAR.2017.20"},{"key":"415_CR71","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1109\/ICDAR.2005.220","volume":"1","author":"S Quiniou","year":"2005","unstructured":"Quiniou, S., Anquetil, E., Carbonnel, S.: Statistical language models for on-line handwritten sentence recognition. ICDAR 1, 516\u2013520 (2005). https:\/\/doi.org\/10.1109\/ICDAR.2005.220","journal-title":"ICDAR"},{"key":"415_CR72","unstructured":"Rahimian, E., Zabihi, S., Atashzar, S.F., Asif, A., Mohammadi, A.: XceptionTime: a novel deep architecture based on depthwise separable convolutions for hand gesture classification. In: arXiv:1911.03803 (2019)"},{"key":"415_CR73","unstructured":"Reed, S.E., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., Rabinovich, A.: Training deep neural networks on noisy labels with bootstrapping. In: ICLR Workshop (2015)"},{"key":"415_CR74","unstructured":"Reimers, N., Gurevych, I.: Optimal hyperparameters for deep LSTM-networks for sequence labeling tasks. In: EMNLP, pp. 338\u2013348. Copenhagen, Denmark (2017)"},{"key":"415_CR75","doi-asserted-by":"publisher","unstructured":"Rijhwani, S., Anastasopoulo, A., Neubig, G.: OCR post correction for endangered language texts. In: EMNLP, pp. 5931\u20135942 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.478","DOI":"10.18653\/v1\/2020.emnlp-main.478"},{"key":"415_CR76","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: MICCAI, Springer, LNCS, vol. 9351, pp. 234\u2013241 (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"415_CR77","doi-asserted-by":"publisher","unstructured":"Scheidl, H., Fiel, S., Sablatnig, R.: Word beam search: a connectionist temporal classification decoding algorithm. In: ICFHR, pp. 253\u2013258. Niagara Falls, NY (2018). https:\/\/doi.org\/10.1109\/ICFHR-2018.2018.00052","DOI":"10.1109\/ICFHR-2018.2018.00052"},{"key":"415_CR78","unstructured":"Schomaker, L.: The ICDAR 2003 informal competition for the recognition of on-line words: the Unipen-ICROW-03 Benchmark Set. In: https:\/\/www.ai.rug.nl\/lambert\/unipen\/icdar-03-competition\/ (2003)"},{"key":"415_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3173574.3173705","volume":"131","author":"M Schrapel","year":"2018","unstructured":"Schrapel, M., Stadler, M.L., Rohs, M.: Pentelligence: combining pen tip motion and writing sounds for handwritten digit recognition. Conf. Hum. Factors Comput. Syst. 131, 1\u201311 (2018). https:\/\/doi.org\/10.1145\/3173574.3173705","journal-title":"Conf. Hum. Factors Comput. Syst."},{"issue":"3","key":"415_CR80","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1016\/0031-3203(95)00102-6","volume":"29","author":"G Seni","year":"1996","unstructured":"Seni, G., Krip\u00e1sundar, V., Srihari, R.K.: Generalizing edit distance to incorporate domain information: handwritten text recognition as a case study. Pattern Recogn. 29(3), 405\u2013414 (1996). https:\/\/doi.org\/10.1016\/0031-3203(95)00102-6","journal-title":"Pattern Recogn."},{"issue":"7","key":"415_CR81","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/34.506798","volume":"18","author":"G Seni","year":"1996","unstructured":"Seni, G., Srihari, R.K., Nasrabadi, N.: Large vocabulary recognition of on-line handwritten cursive words. TPAMI 18(7), 757\u2013762 (1996). https:\/\/doi.org\/10.1109\/34.506798","journal-title":"TPAMI"},{"key":"415_CR82","doi-asserted-by":"publisher","unstructured":"Sharma, A., Ambati, R., Jayagopi, D.B.: Towards faster offline handwriting recognition using temporal convolutional networks. In: NCVPRIPG, pp. 344\u2013354 (2020). https:\/\/doi.org\/10.1109\/ACOMP.2019.00015","DOI":"10.1109\/ACOMP.2019.00015"},{"key":"415_CR83","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114004","author":"A Sharma","year":"2021","unstructured":"Sharma, A., Jayagopi, D.B.: Towards efficient unconstrained handwriting recognition using dilated temporal convolutional network. Expert Syst. Appl. (2021). https:\/\/doi.org\/10.1016\/j.eswa.2020.114004","journal-title":"Expert Syst. Appl."},{"key":"415_CR84","doi-asserted-by":"publisher","unstructured":"Shivram, A., Ramaiah, C., Setlur, S., Govindaraju, V.: IBM_UB_1: a dual mode unconstrained english handwriting dataset. In: ICDAR, pp. 13\u201317 (2013). https:\/\/doi.org\/10.1109\/ICDAR.2013.12","DOI":"10.1109\/ICDAR.2013.12"},{"key":"415_CR85","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s10032-018-0295-0","volume":"21","author":"S Sudholt","year":"2018","unstructured":"Sudholt, S., Fink, G.A.: Attribute CNNs for word spotting in handwritten documents. IJDAR 21, 199\u2013218 (2018). https:\/\/doi.org\/10.1007\/s10032-018-0295-0","journal-title":"IJDAR"},{"key":"415_CR86","unstructured":"Synnaeve, G., Xu, Q., Kahn, J., Likhomanenko, T., Grave, E., Pratap, V., Sriram, A., Liptchinsky, V., Collobert, R.: End-to-End ASR: from supervised to semi-supervised learning with modern architectures. In: ICML Workshop. Vienna, Austria (2020)"},{"key":"415_CR87","doi-asserted-by":"crossref","unstructured":"Tan, C.W., Dempster, A., Bergmeir, C., Webb, G.I.: MultiRocket: multiple pooling operators and transformations for fast and effective time series classification. In: arXiv:2102.00457 (2021)","DOI":"10.1007\/s10618-022-00844-1"},{"key":"415_CR88","doi-asserted-by":"publisher","unstructured":"Tanaka, D., Ikami, D., Yamasaki, T., Aizawa, K.: Joint optimization framework for learning with noisy labels. In: CVPR, pp. 5552\u20135560. Salt Lake CIty, UT (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00582","DOI":"10.1109\/CVPR.2018.00582"},{"key":"415_CR89","doi-asserted-by":"crossref","unstructured":"Tang, W., Long, G., Liu, L., Zhou, T., Jiang, J., Blumenstein, M.: Rethinking 1D-CNN for time series classification: a stronger baseline. In: arXiv:2002.10061 (2020)","DOI":"10.1109\/IJCNN48605.2020.9206860"},{"key":"415_CR90","unstructured":"Tay, Y., Bahri, D., Yang, L., Metzler, D., Juan, D.C.: Sparse Sinkhorn attention. In: arXiv:2002.11296 (2020)"},{"key":"415_CR91","doi-asserted-by":"publisher","unstructured":"Tian, B., Zhang, Y., Wang, J., Xing, C.: Hierarchical inter-attention network for document classification with multi-task learning. In: IJCAI, pp. 3569\u20133575 (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/495","DOI":"10.24963\/ijcai.2019\/495"},{"key":"415_CR92","first-page":"2471","volume":"23","author":"J Uhang","year":"2020","unstructured":"Uhang, J., Du, J., Yang, Y., Song, Y.Z., Dai, L.: SRD: a tree structure based decoder for online handwritten mathematical expression recognition. Trans. Multimed. 23, 2471\u20132480 (2020)","journal-title":"Trans. Multimed."},{"key":"415_CR93","doi-asserted-by":"publisher","unstructured":"Um, T.T., Pfister, F.M.J., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., Kulic, D.: Data augmentation of wearable sensor data for Parkinson\u2019s disease monitoring using convolutional neural networks. In: ICMI, pp. 216\u2013220. Glasgow, UK (2017). https:\/\/doi.org\/10.1145\/3136755.3136817","DOI":"10.1145\/3136755.3136817"},{"key":"415_CR94","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.: Attention is all you need. In: NIPS, pp. 5998\u20136008. Long Beach, CA (2017)"},{"key":"415_CR95","doi-asserted-by":"publisher","unstructured":"Viard-Gaudin, C., Lallican, P.M., Binter, P., Knerr, S.: The IRESTE On\/Off (IRONOFF) dual handwriting database. In: ICDAR, pp. 455\u2013458 (1999). https:\/\/doi.org\/10.1109\/ICDAR.1999.791823","DOI":"10.1109\/ICDAR.1999.791823"},{"key":"415_CR96","doi-asserted-by":"publisher","unstructured":"Vinciarelli, A., Perrone, M.P.: Combining online and offline handwriting recognition. In: ICDAR, pp. 844\u2013848. Edinburgh, UK (2003). https:\/\/doi.org\/10.1109\/ICDAR.2003.1227781","DOI":"10.1109\/ICDAR.2003.1227781"},{"key":"415_CR97","doi-asserted-by":"publisher","unstructured":"Voigtlaender, P., Doetsch, P., Ney, H.: Handwriting recognition with large multidimensional long short-term memory recurrent neural networks. In: ICFHR, pp. 228\u2013233 (2016). https:\/\/doi.org\/10.1109\/ICFHR.2016.0052","DOI":"10.1109\/ICFHR.2016.0052"},{"issue":"10","key":"415_CR98","doi-asserted-by":"publisher","first-page":"3661","DOI":"10.1016\/j.patcog.2012.04.020","volume":"45","author":"DH Wang","year":"2012","unstructured":"Wang, D.H., Liu, C.L., Zhou, X.D.: An approach for real-time recognition of online Chinese handwritten sentences. Pattern Recogn. 45(10), 3661\u20133675 (2012). https:\/\/doi.org\/10.1016\/j.patcog.2012.04.020","journal-title":"Pattern Recogn."},{"key":"415_CR99","doi-asserted-by":"publisher","unstructured":"Wang, J., Wang, Z., Li, J., Wu, J.: A transformer-based framework for multivariate time series representation learning. In: SIGKDD, pp. 2437\u20132446 (2018). https:\/\/doi.org\/10.1145\/3219819.3220060","DOI":"10.1145\/3219819.3220060"},{"key":"415_CR100","doi-asserted-by":"publisher","unstructured":"Wang, J.S., Hsu, Y.L., Chu, C.L.: Online handwriting recognition using an accelerometer-based pen device. In: CSE (2013). https:\/\/doi.org\/10.2991\/cse.2013.52","DOI":"10.2991\/cse.2013.52"},{"key":"415_CR101","unstructured":"Wang, S., Li, B.Z., Khabsa, M., Fang, H., Ma, H.: Linformer: self-attention with linear complexity. In: arXiv:2006.04768 (2020)"},{"key":"415_CR102","doi-asserted-by":"publisher","unstructured":"Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., Bailey, J.: Symmetric cross entropy for robust learning with noisy labels. In: ICCV, pp. 322\u2013330. Seoul, Korea (South) (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00041","DOI":"10.1109\/ICCV.2019.00041"},{"key":"415_CR103","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: arXiv:1611.06455 (2016)","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"415_CR104","doi-asserted-by":"crossref","unstructured":"Wehbi, M., Hamann, T., Barth, J., K\u00e4mpf, P., Zanca, D., Eskofier, B.: Towards an IMU-based pen online handwriting recognizer. In: ICDAR, pp. 289\u2013303 (2021)","DOI":"10.1007\/978-3-030-86334-0_19"},{"key":"415_CR105","doi-asserted-by":"publisher","unstructured":"Wigington, C., Tensmeyer, C., Davis, B., Barrett, W., Price, B., Cohen, S.: Start, follow, read: end-to-end full-page handwriting recognition. In: ECCV, pp. 372\u2013388 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_23","DOI":"10.1007\/978-3-030-01231-1_23"},{"issue":"7","key":"415_CR106","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1177\/0956797621993111","volume":"32","author":"RW Wiley","year":"2021","unstructured":"Wiley, R.W., Rapp, B.: The effects of handwriting experience of literacy learning. Psychol. Sci. 32(7), 1086\u20131103 (2021). https:\/\/doi.org\/10.1177\/0956797621993111","journal-title":"Psychol. Sci."},{"key":"415_CR107","doi-asserted-by":"publisher","unstructured":"Yan, J., Mu, L., Wang, L., Ranjan, R., Zomaya, A.Y.: Temporal convolutional networks for the advance prediction of ENSO. Nat. Sci. Rep. (2020) https:\/\/doi.org\/10.1038\/s41598-020-65070-5","DOI":"10.1038\/s41598-020-65070-5"},{"key":"415_CR108","doi-asserted-by":"publisher","unstructured":"Yana, B., Onoye, T.: Fusion networks for air-writing recognition. In: MMM, pp. 142\u2013152 (2018). https:\/\/doi.org\/10.1007\/978-3-319-73600-6_13","DOI":"10.1007\/978-3-319-73600-6_13"},{"key":"415_CR109","doi-asserted-by":"publisher","unstructured":"Yousef, M., Bishop, T.E.: OrigamiNet: weakly-supervised, segmentation-free, one-step, full page text recognition by learning to unfold. In: CVPR, pp. 14710\u201314719. Seattle, WA (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01472","DOI":"10.1109\/CVPR42600.2020.01472"},{"key":"415_CR110","doi-asserted-by":"publisher","unstructured":"Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., Eickhoff, C.: A transformer-based framework for multivariate time series representation learning. In: SIGKDD, pp. 2114\u20132124 (2021). https:\/\/doi.org\/10.1145\/3447548.3467401","DOI":"10.1145\/3447548.3467401"},{"key":"415_CR111","doi-asserted-by":"publisher","unstructured":"Zhang, X., Gao, Y., Lin, J., Lu, C.T.: TapNet: multivariate time series classification with attentional prototypical network. In: AAAI, pp. 6845\u20136852 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i04.6165","DOI":"10.1609\/aaai.v34i04.6165"},{"key":"415_CR112","unstructured":"Zhang, Z., Sabuncu, M.R.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: NIPS, pp. 8778\u20138788. Montr\u00e9al, Canada (2018)"},{"key":"415_CR113","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neucom.2019.08.023","volume":"367","author":"X Zou","year":"2019","unstructured":"Zou, X., Wang, Z., Li, Q., Sheng, W.: Integration of residual network and convolutional neural network along with various activation functions and global pooling for time series classification. Neurocomputing 367, 39\u201345 (2019)","journal-title":"Neurocomputing"}],"container-title":["International Journal on Document Analysis and Recognition (IJDAR)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10032-022-00415-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10032-022-00415-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10032-022-00415-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T12:32:49Z","timestamp":1728045169000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10032-022-00415-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,22]]},"references-count":113,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["415"],"URL":"https:\/\/doi.org\/10.1007\/s10032-022-00415-6","relation":{},"ISSN":["1433-2833","1433-2825"],"issn-type":[{"type":"print","value":"1433-2833"},{"type":"electronic","value":"1433-2825"}],"subject":[],"published":{"date-parts":[[2022,9,22]]},"assertion":[{"value":"31 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"see Sect.\u00a0","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"see Sect.\u00a0","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}