{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T04:30:15Z","timestamp":1730349015499,"version":"3.28.0"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"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":["IJDAR"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s10032-024-00460-3","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T11:02:27Z","timestamp":1708426947000},"page":"567-581","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["On the improvement of handwritten text line recognition with octave convolutional recurrent neural networks"],"prefix":"10.1007","volume":"27","author":[{"given":"Dayvid","family":"Castro","sequence":"first","affiliation":[]},{"given":"Cleber","family":"Zanchettin","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds A. Nunes","family":"Amaral","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"key":"460_CR1","unstructured":"Augustin, E., Carr\u00e9, M., Grosicki, E., et\u00a0al.: Rimes evaluation campaign for handwritten mail processing. In: International Workshop on Frontiers in Handwriting Recognition (IWFHR\u201906), pp 231\u2013235 (2006)"},{"key":"460_CR2","doi-asserted-by":"crossref","unstructured":"Barrere, K., Soullard, Y., Lemaitre, A., et\u00a0al.: A light transformer-based architecture for handwritten text recognition. In: International Workshop on Document Analysis Systems, Springer, pp 275\u2013290 (2022)","DOI":"10.1007\/978-3-031-06555-2_19"},{"key":"460_CR3","unstructured":"Bauer, L.: Manual of information to accompany the Wellington corpus of written New Zealand English. Victoria University of Wellington Wellington, Department of Linguistics (1993)"},{"key":"460_CR4","unstructured":"Bluche, T.: Joint line segmentation and transcription for end-to-end handwritten paragraph recognition. Adv. Neural Inf. Proc. Syst. 29 (2016)"},{"key":"460_CR5","doi-asserted-by":"crossref","unstructured":"Bluche, T., Messina, R.: Gated convolutional recurrent neural networks for multilingual handwriting recognition. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), IEEE, pp 646\u2013651 (2017)","DOI":"10.1109\/ICDAR.2017.111"},{"key":"460_CR6","doi-asserted-by":"crossref","unstructured":"Bluche, T., Louradour, J., Knibbe, M., et\u00a0al.: The a2ia arabic handwritten text recognition system at the open hart2013 evaluation. In: 2014 11th IAPR International Workshop on Document Analysis Systems, IEEE, pp 161\u2013165 (2014)","DOI":"10.1109\/DAS.2014.40"},{"key":"460_CR7","doi-asserted-by":"crossref","unstructured":"Bluche, T., Louradour, J., Messina, R.: Scan, attend and read: End-to-end handwritten paragraph recognition with mdlstm attention. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), IEEE, pp 1050\u20131055 (2017)","DOI":"10.1109\/ICDAR.2017.174"},{"key":"460_CR8","doi-asserted-by":"crossref","unstructured":"Cascianelli, S., Cornia, M., Baraldi, L., et\u00a0al.: Boosting modern and historical handwritten text recognition with deformable convolutions. Int. J. Doc. Anal. Recognit. (IJDAR) pp 1\u201311 (2022)","DOI":"10.1007\/s10032-022-00401-y"},{"key":"460_CR9","doi-asserted-by":"crossref","unstructured":"Castro, D., Bezerra, B.L., Valenca, M.: Boosting the deep multidimensional long-short-term memory network for handwritten recognition systems. In: 2018 16th international conference on frontiers in handwriting recognition (ICFHR), IEEE, pp 127\u2013132 (2018)","DOI":"10.1109\/ICFHR-2018.2018.00031"},{"key":"460_CR10","doi-asserted-by":"crossref","unstructured":"Cheddad, A., Kusetogullari, H., Hilmkil, A., et\u00a0al.: Shibr-the swedish historical birth records: a semi-annotated dataset. Neural Comput. Appl. 1\u201313 (2021)","DOI":"10.1007\/s00521-021-06207-z"},{"issue":"4","key":"460_CR11","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1006\/csla.1999.0128","volume":"13","author":"SF Chen","year":"1999","unstructured":"Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. Comput. Speech Language 13(4), 359\u2013394 (1999)","journal-title":"Comput. Speech Language"},{"key":"460_CR12","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fan, H., Xu, B., et\u00a0al.: Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 3435\u20133444 (2019)","DOI":"10.1109\/ICCV.2019.00353"},{"key":"460_CR13","doi-asserted-by":"crossref","unstructured":"Coquenet, D., Soullard, Y., Chatelain, C., et\u00a0al.: Have convolutions already made recurrence obsolete for unconstrained handwritten text recognition? In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), IEEE, pp 65\u201370 (2019)","DOI":"10.1109\/ICDARW.2019.40083"},{"key":"460_CR14","doi-asserted-by":"crossref","unstructured":"Coquenet, D., Chatelain, C., Paquet, T.: Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, pp 19\u201324 (2020)","DOI":"10.1109\/ICFHR2020.2020.00015"},{"issue":"1","key":"460_CR15","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/TPAMI.2022.3144899","volume":"45","author":"D Coquenet","year":"2022","unstructured":"Coquenet, D., Chatelain, C., Paquet, T.: End-to-end handwritten paragraph text recognition using a vertical attention network. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 508\u2013524 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"460_CR16","doi-asserted-by":"crossref","unstructured":"Coquenet, D., Chatelain, C., Paquet, T.: Dan: a segmentation-free document attention network for handwritten document recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3235826"},{"key":"460_CR17","doi-asserted-by":"crossref","unstructured":"Doetsch, P., Kozielski, M., Ney, H.: Fast and robust training of recurrent neural networks for offline handwriting recognition. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, IEEE, pp 279\u2013284 (2014)","DOI":"10.1109\/ICFHR.2014.54"},{"key":"460_CR18","doi-asserted-by":"crossref","unstructured":"Dreuw, P., Doetsch, P., Plahl, C., et\u00a0al.: Hierarchical hybrid mlp\/hmm or rather mlp features for a discriminatively trained gaussian hmm: a comparison for offline handwriting recognition. In: Image Processing (ICIP), 2011 18th IEEE International Conference on, IEEE, pp 3541\u20133544 (2011)","DOI":"10.1109\/ICIP.2011.6116480"},{"key":"460_CR19","doi-asserted-by":"crossref","unstructured":"Dutta, K., Krishnan, P., Mathew, M., et\u00a0al.: Improving cnn-rnn hybrid networks for handwriting recognition. In: 2018 16th international conference on frontiers in handwriting recognition (ICFHR), IEEE, pp 80\u201385 (2018)","DOI":"10.1109\/ICFHR-2018.2018.00023"},{"issue":"397","key":"460_CR20","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1080\/01621459.1987.10478410","volume":"82","author":"B Efron","year":"1987","unstructured":"Efron, B.: Better bootstrap confidence intervals. J. Am. Stat. Assoc. 82(397), 171\u2013185 (1987)","journal-title":"J. Am. Stat. Assoc."},{"issue":"4","key":"460_CR21","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1109\/TPAMI.2010.141","volume":"33","author":"S Espana-Boquera","year":"2010","unstructured":"Espana-Boquera, S., Castro-Bleda, M.J., Gorbe-Moya, J., et al.: Improving offline handwritten text recognition with hybrid hmm\/ann models. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 767\u2013779 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"460_CR22","volume-title":"A manual of information to accompany A standard sample of present-day edited American English, for use with digital computers","author":"WN Francis","year":"1971","unstructured":"Francis, W.N.: A manual of information to accompany A standard sample of present-day edited American English, for use with digital computers. Brown University, Department of Linguistics (1971)"},{"key":"460_CR23","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT press (2016)"},{"key":"460_CR24","first-page":"545","volume":"21","author":"A Graves","year":"2008","unstructured":"Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. Adv. Neural Inf. Proc. Syst. 21, 545\u2013552 (2008)","journal-title":"Adv. Neural Inf. Proc. Syst."},{"key":"460_CR25","doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F., et\u00a0al.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd international conference on Machine learning, pp 369\u2013376 (2006)","DOI":"10.1145\/1143844.1143891"},{"issue":"5","key":"460_CR26","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TPAMI.2008.137","volume":"31","author":"A Graves","year":"2008","unstructured":"Graves, A., Liwicki, M., Fern\u00e1ndez, S., et al.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855\u2013868 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"460_CR27","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"460_CR28","doi-asserted-by":"crossref","unstructured":"Ingle, R.R., Fujii, Y., Deselaers, T., et\u00a0al.: A scalable handwritten text recognition system. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp 17\u201324 (2019)","DOI":"10.1109\/ICDAR.2019.00013"},{"key":"460_CR29","doi-asserted-by":"crossref","unstructured":"Jaramillo, J.C.A., Murillo-Fuentes, J.J., Olmos, P.M.: Boosting handwriting text recognition in small databases with transfer learning. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, pp 429\u2013434 (2018)","DOI":"10.1109\/ICFHR-2018.2018.00081"},{"key":"460_CR30","volume-title":"The Tagged LOB Corpus","author":"S Johansson","year":"1986","unstructured":"Johansson, S., Eric, A., Roger, G., et al.: The Tagged LOB Corpus. Users\u2019 Manual, Norwegian Computing Centre for the Humanities, Bergen (1986)"},{"key":"460_CR31","doi-asserted-by":"crossref","unstructured":"Kang, L., Riba, P., Rusi\u00f1ol, M., et al.: Pay attention to what you read: non-recurrent handwritten text-line recognition. Pattern Recognition 129, 108766 (2022)","DOI":"10.1016\/j.patcog.2022.108766"},{"key":"460_CR32","doi-asserted-by":"crossref","unstructured":"Knerr, S., Augustin, E.: A neural network-hidden markov model hybrid for cursive word recognition. In: Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on, IEEE, pp 1518\u20131520 (1998)","DOI":"10.1109\/ICPR.1998.711996"},{"key":"460_CR33","doi-asserted-by":"crossref","unstructured":"Koerich, A.L., Leydier, Y., Sabourin, R., et\u00a0al.: A hybrid large vocabulary handwritten word recognition system using neural networks with hidden markov models. In: Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop on, IEEE, pp 99\u2013104 (2002)","DOI":"10.1109\/IWFHR.2002.1030893"},{"key":"460_CR34","doi-asserted-by":"crossref","unstructured":"Kozielski, M., Doetsch, P., Ney, H., et\u00a0al.: Improvements in rwth\u2019s system for off-line handwriting recognition. In: 2013 12th International Conference on Document Analysis and Recognition, IEEE, pp 935\u2013939 (2013)","DOI":"10.1109\/ICDAR.2013.190"},{"issue":"11","key":"460_CR35","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"460_CR36","unstructured":"Levenshtein, V.I., et\u00a0al.: Binary codes capable of correcting deletions, insertions, and reversals. In: Soviet physics doklady, Soviet Union, pp 707\u2013710 (1966)"},{"key":"460_CR37","unstructured":"Li, M., Lv, T., Chen, J., et\u00a0al.: Trocr: Transformer-based optical character recognition with pre-trained models. arXiv preprint arXiv:2109.10282 (2021)"},{"key":"460_CR38","unstructured":"Lindeberg, T.: Scale-space theory in computer vision, vol 256. Springer Science & Business Media (2013)"},{"key":"460_CR39","unstructured":"Lins, R.: Nabuco\u2013two decades of processing historical documents in latin america. J. Univ. Comput. Sci. (2011)"},{"key":"460_CR40","doi-asserted-by":"crossref","unstructured":"Ly, N.T., Ngo, T.T., Nakagawa, M.: A self-attention based model for offline handwritten text recognition. In: Asian Conference on Pattern Recognition, Springer, pp 356\u2013369 (2022)","DOI":"10.1007\/978-3-031-02444-3_27"},{"key":"460_CR41","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y., et\u00a0al.: Rectifier nonlinearities improve neural network acoustic models. In: Proc. icml, Citeseer, p\u00a03 (2013)"},{"key":"460_CR42","doi-asserted-by":"crossref","unstructured":"Marti, U.V., Bunke, H.: Using a statistical language model to improve the performance of an hmm-based cursive handwriting recognition system. In: Hidden Markov models: applications in computer vision. World Scientific, p 65\u201390 (2001)","DOI":"10.1142\/9789812797605_0004"},{"issue":"1","key":"460_CR43","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":"460_CR44","doi-asserted-by":"crossref","unstructured":"Michael, J., Labahn, R., Gr\u00fcning, T., et\u00a0al.: Evaluating sequence-to-sequence models for handwritten text recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp 1286\u20131293 (2019)","DOI":"10.1109\/ICDAR.2019.00208"},{"issue":"2","key":"460_CR45","doi-asserted-by":"publisher","first-page":"023028","DOI":"10.1117\/1.JEI.22.2.023028","volume":"22","author":"O Morillot","year":"2013","unstructured":"Morillot, O., Likforman-Sulem, L., Grosicki, E.: New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks. J. Electronic Imaging 22(2), 023028\u2013023028 (2013)","journal-title":"J. Electronic Imaging"},{"issue":"3","key":"460_CR46","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s10032-019-00325-0","volume":"22","author":"B Moysset","year":"2019","unstructured":"Moysset, B., Messina, R.: Are 2d-lstm really dead for offline text recognition? Int. J. Doc. Anal. Recognit. (IJDAR) 22(3), 193\u2013208 (2019)","journal-title":"Int. J. Doc. Anal. Recognit. (IJDAR)"},{"key":"460_CR47","doi-asserted-by":"crossref","unstructured":"Moysset, B., Bluche, T., Knibbe, M., et\u00a0al.: The a2ia multi-lingual text recognition system at the second maurdor evaluation. In: Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on, IEEE, pp 297\u2013302 (2014)","DOI":"10.1109\/ICFHR.2014.57"},{"key":"460_CR48","unstructured":"Muehlberger, G., Seaward, L., Terras, M., et\u00a0al.: Transforming scholarship in the archives through handwritten text recognition: Transkribus as a case study. J. Doc. (2019)"},{"key":"460_CR49","unstructured":"Paszke, A., Gross, S., Chintala, S., et\u00a0al.: Automatic differentiation in pytorch. In: NIPS-W (2017)"},{"key":"460_CR50","doi-asserted-by":"crossref","unstructured":"Pham, V., Bluche, T., Kermorvant, C., et\u00a0al.: Dropout improves recurrent neural networks for handwriting recognition. In: 2014 14th international conference on frontiers in handwriting recognition, IEEE, pp 285\u2013290 (2014)","DOI":"10.1109\/ICFHR.2014.55"},{"issue":"4","key":"460_CR51","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s10032-009-0098-4","volume":"12","author":"T Pl\u00f6tz","year":"2009","unstructured":"Pl\u00f6tz, T., Fink, G.A.: Markov models for offline handwriting recognition: a survey. Int. J. Doc. Anal. Recognit. (IJDAR) 12(4), 269 (2009)","journal-title":"Int. J. Doc. Anal. Recognit. (IJDAR)"},{"key":"460_CR52","doi-asserted-by":"crossref","unstructured":"Poulos, J., Valle, R.: Character-based handwritten text transcription with attention networks. Neural Comput. Appl. pp 1\u201311 (2021)","DOI":"10.1007\/s00521-021-05813-1"},{"key":"460_CR53","unstructured":"Povey, D., Ghoshal, A., Boulianne, G., et\u00a0al.: The kaldi speech recognition toolkit. In: IEEE 2011 workshop on automatic speech recognition and understanding, IEEE Signal Processing Society, CONF (2011)"},{"key":"460_CR54","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), IEEE, pp 67\u201372 (2017)","DOI":"10.1109\/ICDAR.2017.20"},{"key":"460_CR55","unstructured":"Raschka, S.: Model evaluation, model selection, and algorithm selection in machine learning. arXiv preprint arXiv:1811.12808 (2018)"},{"key":"460_CR56","doi-asserted-by":"crossref","unstructured":"Sanchez, J.A., Toselli, A.H., Romero, V., et\u00a0al.: Icdar 2015 competition htrts: Handwritten text recognition on the transcriptorium dataset. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp 1166\u20131170 (2015)","DOI":"10.1109\/ICDAR.2015.7333944"},{"key":"460_CR57","doi-asserted-by":"crossref","unstructured":"Sanchez, J.A., Romero, V., Toselli, A.H., et\u00a0al.: Icfhr2016 competition on handwritten text recognition on the read dataset. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, pp 630\u2013635 (2016)","DOI":"10.1109\/ICFHR.2016.0120"},{"key":"460_CR58","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., et al.: A set of benchmarks for handwritten text recognition on historical documents. Pattern Recognit. 94, 122\u2013134 (2019)","journal-title":"Pattern Recognit."},{"issue":"2","key":"460_CR59","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/S0031-3203(99)00055-2","volume":"33","author":"J Sauvola","year":"2000","unstructured":"Sauvola, J., Pietik\u00e4inen, M.: Adaptive document image binarization. Pattern Recognit. 33(2), 225\u2013236 (2000)","journal-title":"Pattern Recognit."},{"key":"460_CR60","doi-asserted-by":"crossref","unstructured":"Sharma, A., Jayagopi, D.B.: Towards efficient unconstrained handwriting recognition using dilated temporal convolution network. Expert Systems with Applications 164, 114004 (2021)","DOI":"10.1016\/j.eswa.2020.114004"},{"key":"460_CR61","doi-asserted-by":"crossref","unstructured":"Singh, S.S., Karayev, S.: Full page handwriting recognition via image to sequence extraction. In: Document Analysis and Recognition\u2013ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5\u201310, 2021, Proceedings, Part III 16, Springer, pp 55\u201369 (2021)","DOI":"10.1007\/978-3-030-86334-0_4"},{"key":"460_CR62","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1109\/SIBGRAPI51738.2020.00016","volume-title":"2020 33rd SIBGRAPI Conference on Graphics","author":"AF de Sousa Neto","year":"2020","unstructured":"de Sousa Neto, A.F., Bezerra, B.L.D., Toselli, A.H., et al.: Htr-flor: A deep learning system for offline handwritten text recognition. In: 2020 33rd SIBGRAPI Conference on Graphics, pp. 54\u201361. Patterns and Images (SIBGRAPI), IEEE (2020)"},{"key":"460_CR63","doi-asserted-by":"crossref","unstructured":"Stolcke, A.: Srilm-an extensible language modeling toolkit. In: Seventh international conference on spoken language processing (2002)","DOI":"10.21437\/ICSLP.2002-303"},{"key":"460_CR64","unstructured":"Stuner, B., Chatelain, C., Paquet, T.: Lv-rover: lexicon verified recognizer output voting error reduction. arXiv preprint arXiv:1707.07432 (2017)"},{"key":"460_CR65","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning, PMLR, pp 3319\u20133328 (2017)"},{"key":"460_CR66","doi-asserted-by":"crossref","unstructured":"Tassopoulou, V., Retsinas, G., Maragos, P.: Enhancing handwritten text recognition with n-gram sequence decomposition and multitask learning. In: 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, pp 10555\u201310560 (2021)","DOI":"10.1109\/ICPR48806.2021.9412351"},{"key":"460_CR67","doi-asserted-by":"crossref","unstructured":"Tay, Y.H., Khalid, M., Yusof, R., et\u00a0al.: Offline cursive handwriting recognition system based on hybrid markov model and neural networks. In: Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium on, IEEE, pp 1190\u20131195 (2003)","DOI":"10.1109\/CIRA.2003.1222166"},{"key":"460_CR68","unstructured":"Tieleman, T., Hinton, G., et\u00a0al.: Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning 4(2):26\u201331 (2012)"},{"issue":"2","key":"460_CR69","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1109\/TIT.1967.1054010","volume":"13","author":"A Viterbi","year":"1967","unstructured":"Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory 13(2), 260\u2013269 (1967)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"460_CR70","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., Doetsch, P., Ney, H.: Handwriting recognition with large multidimensional long short-term memory recurrent neural networks. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, pp 228\u2013233 (2016)","DOI":"10.1109\/ICFHR.2016.0052"},{"key":"460_CR71","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xiao, W., Li, S.: Offline handwritten text recognition using deep learning: A review. In: Journal of Physics: Conference Series, IOP Publishing, p 012015 (2021)","DOI":"10.1088\/1742-6596\/1848\/1\/012015"},{"key":"460_CR72","doi-asserted-by":"crossref","unstructured":"Wick, C., Z\u00f6llner, J., Gr\u00fcning, T.: Transformer for handwritten text recognition using bidirectional post-decoding. In: International Conference on Document Analysis and Recognition, Springer, pp 112\u2013126 (2021)","DOI":"10.1007\/978-3-030-86334-0_8"},{"key":"460_CR73","doi-asserted-by":"crossref","unstructured":"Wick, C., Z\u00f6llner, J., Gr\u00fcning, T.: Rescoring sequence-to-sequence models for text line recognition with ctc-prefixes. In: International Workshop on Document Analysis Systems, Springer, pp 260\u2013274 (2022)","DOI":"10.1007\/978-3-031-06555-2_18"},{"key":"460_CR74","doi-asserted-by":"crossref","unstructured":"Wigington, C., Tensmeyer, C., Davis, B., et\u00a0al.: Start, follow, read: End-to-end full-page handwriting recognition. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 367\u2013383 (2018)","DOI":"10.1007\/978-3-030-01231-1_23"},{"key":"460_CR75","doi-asserted-by":"crossref","unstructured":"Wu, Y.C., Yin, F., Chen, Z., et\u00a0al.: Handwritten chinese text recognition using separable multi-dimensional recurrent neural network. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), IEEE, pp 79\u201384 (2017)","DOI":"10.1109\/ICDAR.2017.22"},{"key":"460_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00133-y","volume":"1","author":"S Xiao","year":"2020","unstructured":"Xiao, S., Peng, L., Yan, R., et al.: Deep network with pixel-level rectification and robust training for handwriting recognition. SN Comput. Sci. 1, 1\u201313 (2020)","journal-title":"SN Comput. Sci."},{"key":"460_CR77","doi-asserted-by":"crossref","unstructured":"Yousef, M., Bishop, T.E.: Origaminet: weakly-supervised, segmentation-free, one-step, full page text recognition by learning to unfold. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 14710\u201314719 (2020)","DOI":"10.1109\/CVPR42600.2020.01472"},{"key":"460_CR78","doi-asserted-by":"crossref","unstructured":"Yousef, M., Hussain, K.F., Mohammed, U.S.: Accurate, data-efficient, unconstrained text recognition with convolutional neural networks. Pattern Recognition 108, 107482 (2020)","DOI":"10.1016\/j.patcog.2020.107482"}],"container-title":["International Journal on Document Analysis and Recognition (IJDAR)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10032-024-00460-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10032-024-00460-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10032-024-00460-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T17:05:17Z","timestamp":1730307917000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10032-024-00460-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,20]]},"references-count":78,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["460"],"URL":"https:\/\/doi.org\/10.1007\/s10032-024-00460-3","relation":{},"ISSN":["1433-2833","1433-2825"],"issn-type":[{"type":"print","value":"1433-2833"},{"type":"electronic","value":"1433-2825"}],"subject":[],"published":{"date-parts":[[2024,2,20]]},"assertion":[{"value":"1 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}