{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:56:43Z","timestamp":1776182203374,"version":"3.50.1"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T00:00:00Z","timestamp":1614384000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T00:00:00Z","timestamp":1614384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DGE 1106400"],"award-info":[{"award-number":["DGE 1106400"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS 1638521"],"award-info":[{"award-number":["DMS 1638521"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s00521-021-05813-1","type":"journal-article","created":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T12:02:56Z","timestamp":1614427376000},"page":"10563-10573","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Character-based handwritten text transcription with attention networks"],"prefix":"10.1007","volume":"33","author":[{"given":"Jason","family":"Poulos","sequence":"first","affiliation":[]},{"given":"Rafael","family":"Valle","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"key":"5813_CR1","doi-asserted-by":"crossref","unstructured":"Bluche T, Louradour J, Messina R (2016) Scan, attend and read: end-to-end handwritten paragraph recognition with MDLSTM attention. ArXiv e-prints 1604:03286","DOI":"10.1109\/ICDAR.2017.174"},{"key":"5813_CR2","doi-asserted-by":"crossref","unstructured":"Louradour J, Kermorvant C (2013) Curriculum learning for handwritten text line recognition. ArXiv e-prints 1312:1737","DOI":"10.1109\/DAS.2014.38"},{"key":"5813_CR3","doi-asserted-by":"crossref","unstructured":"Graves A, Fern\u00e1ndez S, Gomez F, Schmidhuber J (2006) Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd international conference on Machine learning, pp 369\u2013376","DOI":"10.1145\/1143844.1143891"},{"key":"5813_CR4","first-page":"855","volume":"31","author":"A Graves","year":"2009","unstructured":"Graves A, Liwicki M, Fern\u00e1ndez S, Bertolami R, Bunke H, Schmidhuber J (2009) A novel connectionist dystem for unconstrained handwriting recognition. IEEE 31:855\u2013868","journal-title":"IEEE"},{"key":"5813_CR5","unstructured":"Liwicki M, Graves A, Bunke H, Schmidhuber J (2007) A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks. In: Proceedings of the 9th International conference on document analysis and recognition, vol\u00a01, pp 367\u2013371"},{"key":"5813_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-642-24049-2_2","volume-title":"Computational intelligence paradigms in advanced pattern classification","author":"M Liwicki","year":"2012","unstructured":"Liwicki M, Graves A, Bunke H (2012) Neural networks for handwriting recognition. Computational intelligence paradigms in advanced pattern classification. Springer, Berlin, pp 5\u201324"},{"key":"5813_CR7","doi-asserted-by":"crossref","unstructured":"Wigington C, Stewart S, Davis B, Barrett B, Price B, Cohen S (2017) 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), IEEE, vol\u00a01, pp 639\u2013645","DOI":"10.1109\/ICDAR.2017.110"},{"issue":"45","key":"5813_CR8","doi-asserted-by":"publisher","first-page":"34407","DOI":"10.1007\/s11042-020-09198-6","volume":"79","author":"B Stuner","year":"2020","unstructured":"Stuner B, Chatelain C, Paquet T (2020) Handwriting recognition using cohort of lstm and lexicon verification with extremely large lexicon. Multim Tools Appl 79(45):34407\u201334427","journal-title":"Multim Tools Appl"},{"key":"5813_CR9","unstructured":"Deng Y, Kanervisto A, Ling J, Rush AM (2016) Image-to-Markup Generation with Coarse-to-Fine Attention. ArXiv e-prints 1609:04938"},{"key":"5813_CR10","unstructured":"Vinyals O, Kaiser L, Koo T, Petrov S, Sutskever I, Hinton G (2014) Grammar as a Foreign Language. ArXiv e-prints 1412:7449"},{"key":"5813_CR11","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. ArXiv e-prints 1409:0473"},{"key":"5813_CR12","unstructured":"Chorowski JK, Bahdanau D, Serdyuk D, Cho K, Bengio Y (2015) Attention-based models for speech recognition. In: Advances in neural information processing systems, pp 577\u2013585"},{"key":"5813_CR13","first-page":"77","volume":"14","author":"K Xu","year":"2015","unstructured":"Xu K, Ba J, Kiros R, Cho K, Courville AC, Salakhutdinov R, Zemel RS, Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention. ICML 14:77\u201381","journal-title":"ICML"},{"key":"5813_CR14","doi-asserted-by":"crossref","unstructured":"Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. ArXiv e-prints 1406:1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"5813_CR15","doi-asserted-by":"crossref","unstructured":"Cho K, Courville A, Bengio Y (2015) Describing multimedia content using attention-based encoder-decoder networks. ArXiv e-prints 1507:01053","DOI":"10.1109\/TMM.2015.2477044"},{"key":"5813_CR16","doi-asserted-by":"crossref","unstructured":"Lee CY, Osindero S (2016) Recursive recurrent nets with attention modeling for OCR in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2231\u20132239","DOI":"10.1109\/CVPR.2016.245"},{"key":"5813_CR17","doi-asserted-by":"crossref","unstructured":"Shi B, Wang X, Lyu P, Yao C, Bai X (2016) Robust scene text recognition with automatic rectification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4168\u20134176","DOI":"10.1109\/CVPR.2016.452"},{"key":"5813_CR18","doi-asserted-by":"crossref","unstructured":"Bluche T, Messina R (2017) Gated convolutional recurrent neural networks for multilingual handwriting recognition. In: Proceedings of the 13th International conference on document analysis and recognition (ICDAR), Kyoto, Japan, pp 13\u201315","DOI":"10.1109\/ICDAR.2017.111"},{"key":"5813_CR19","doi-asserted-by":"crossref","unstructured":"Puigcerver J (2017) Are multidimensional recurrent layers really necessary for handwritten text recognition? In: Document analysis and recognition (ICDAR), 2017 14th IAPR international conference on, IEEE, vol\u00a01, pp 67\u201372","DOI":"10.1109\/ICDAR.2017.20"},{"key":"5813_CR20","unstructured":"Chowdhury A, Vig L (2018) An efficient end-to-end neural model for handwritten text recognition. ArXiv e-prints 1807.07965"},{"key":"5813_CR21","doi-asserted-by":"crossref","unstructured":"Zhang Y, Nie S, Liu W, Xu X, Zhang D, Shen HT (2019) Sequence-to-sequence domain adaptation network for robust text image recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00285"},{"key":"5813_CR22","unstructured":"Kang L, Riba P, Villegas M, Forn\u00e9s A, Rusi\u00f1ol M (2019) Candidate fusion: Integrating language modelling into a sequence-to-sequence handwritten word recognition architecture. ArXiv e-prints 1912.10308"},{"key":"5813_CR23","doi-asserted-by":"crossref","unstructured":"Kang L, Rusi\u00f1ol M, Forn\u00e9s A, Riba P, Villegas M (2020) Unsupervised writer adaptation for synthetic-to-real handwritten word recognition. In: The IEEE winter conference on applications of computer vision, pp 3502\u20133511","DOI":"10.1109\/WACV45572.2020.9093392"},{"issue":"3","key":"5813_CR24","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, Wang S (2020) Deep network with pixel-level rectification and robust training for handwriting recognition. SN Comput Sci 1(3):1\u201313","journal-title":"SN Comput Sci"},{"key":"5813_CR25","unstructured":"Retsinas G, Sfikas G, Maragos P (2020) Wsrnet: Joint spotting and recognition of handwritten words. ArXiv e-prints 1604:032860"},{"key":"5813_CR26","doi-asserted-by":"crossref","unstructured":"Belay B, Habtegebrial T, Belay G, Mesheshsa M, Liwicki M, Stricker D (2020) Learning by injection: Attention embedded recurrent neural network for amharic text-image recognition. 1604:032861","DOI":"10.1109\/ICIP.2019.8804407"},{"key":"5813_CR27","doi-asserted-by":"crossref","unstructured":"Sueiras J, Ruiz V, Sanchez A, Velez JF (2018) Offline continuous handwriting recognition using sequence to sequence neural networks. Neurocomputing","DOI":"10.1016\/j.neucom.2018.02.008"},{"key":"5813_CR28","doi-asserted-by":"crossref","unstructured":"Kang L, Toledo JI, Riba P, Villegas M, Forn\u00e9s A, Rusinol M (2018) Convolve, attend and spell: An attention-based sequence-to-sequence model for handwritten word recognition. In: German Conference on Pattern Recognition. pp 459\u2013472. Springer, Berlin","DOI":"10.1007\/978-3-030-12939-2_32"},{"key":"5813_CR29","unstructured":"Gui L, Liang X, Chang X, Hauptmann AG (2018) Adaptive context-aware reinforced agent for handwritten text recognition. In: Proceedings of the British machine vision conference (BMVC)"},{"key":"5813_CR30","unstructured":"Gehring J, Auli M, Grangier D, Yarats D, Dauphin YN (2017) Convolutional sequence to sequence learning. ArXiv e-prints 1604:032862"},{"key":"5813_CR31","doi-asserted-by":"crossref","unstructured":"Fogel S, Averbuch-Elor H, Cohen S, Mazor S, Litman R (2020) Scrabblegan: Semi-supervised varying length handwritten text generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00438"},{"key":"5813_CR32","unstructured":"Davis B, Tensmeyer C, Price B, Wigington C, Morse B, Jain R (2020) Text and style conditioned GAN for generation of offline handwriting lines. ArXiv e-prints 1604:032863"},{"key":"5813_CR33","doi-asserted-by":"crossref","unstructured":"Poznanski A, Wolf L (2016) CNN-n-gram for handwriting word recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2305\u20132314","DOI":"10.1109\/CVPR.2016.253"},{"key":"5813_CR34","doi-asserted-by":"crossref","unstructured":"Such FP, Peri D, Brockler F, Paul H, Ptucha R (2018) Fully convolutional networks for handwriting recognition. In: 2018 16th International conference on frontiers in handwriting recognition (ICFHR), IEEE, pp 86\u201391","DOI":"10.1109\/ICFHR-2018.2018.00024"},{"key":"5813_CR35","doi-asserted-by":"crossref","unstructured":"Coquenet D, Soullard Y, Chatelain C, Paquet T (2019) Have convolutions already made recurrence obsolete for unconstrained handwritten text recognition? In: 2019 International conference on document analysis and recognition workshops (ICDARW), IEEE, vol\u00a05, pp 65\u201370","DOI":"10.1109\/ICDARW.2019.40083"},{"key":"5813_CR36","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.patcog.2018.12.017","volume":"88","author":"R Ptucha","year":"2019","unstructured":"Ptucha R, Such FP, Pillai S, Brockler F, Singh V, Hutkowski P (2019) Intelligent character recognition using fully convolutional neural networks. Pattern Recogn 88:604\u2013613","journal-title":"Pattern Recogn"},{"key":"5813_CR37","doi-asserted-by":"publisher","first-page":"107482","DOI":"10.1016\/j.patcog.2020.107482","volume":"108","author":"M Yousef","year":"2020","unstructured":"Yousef M, Hussain KF, Mohammed US (2020) Accurate, data-efficient, yunconstrained text recognition with convolutional neural networks. Pattern Recogn 108:107482","journal-title":"Pattern Recogn"},{"key":"5813_CR38","doi-asserted-by":"crossref","unstructured":"Yousef M, Bishop TE (2020) 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 (CVPR)","DOI":"10.1109\/CVPR42600.2020.01472"},{"key":"5813_CR39","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inf Process Syst"},{"key":"5813_CR40","unstructured":"Kang L, Riba P, Rusi\u00f1ol M, Forn\u00e9s A, Villegas M (2020) Pay attention to what you read: Non-recurrent handwritten text-line recognition. ArXiv e-prints 1604:032864"},{"key":"5813_CR41","unstructured":"Ling W, Trancoso I, Dyer C, Black AW (2015) Character-based neural machine translation. ArXiv e-prints 1604:032865"},{"issue":"1","key":"5813_CR42","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s100320200071","volume":"5","author":"UV Marti","year":"2002","unstructured":"Marti UV, Bunke H (2002) The IAM-database: an english sentence database for offline handwriting recognition. Int J Doc Anal Recogn 5(1):39\u201346","journal-title":"Int J Doc Anal Recogn"},{"key":"5813_CR43","doi-asserted-by":"crossref","unstructured":"Grosicki E, El-Abed H (2011) ICDAR 2011: French handwriting recognition competition. In: Proceedings of the international conference on document analysis and recognition, pp 1459\u20131463","DOI":"10.1109\/ICDAR.2011.290"},{"key":"5813_CR44","doi-asserted-by":"crossref","unstructured":"Fischer A, Frinken V, Forn\u00e9s A, Bunke H (2011) Transcription alignment of Latin manuscripts using Hidden Markov Models. In: Proceedings of the 2011 workshop on historical document imaging and processing, ACM, pp 29\u201336","DOI":"10.1145\/2037342.2037348"},{"key":"5813_CR45","doi-asserted-by":"crossref","unstructured":"Fischer A, Wuthrich M, Liwicki M, Frinken V, Bunke H, Viehhauser G, Stolz M (2009) Automatic transcription of handwritten medieval documents. In: 2009 15th international conference on virtual systems and multimedia, IEEE, pp 137\u2013142","DOI":"10.1109\/VSMM.2009.26"},{"key":"5813_CR46","unstructured":"Puigcerver J, Martin-Albo D, Villegas M (2016) Laia: A deep learning toolkit for HTR. 1604:032866, gitHub repository"},{"key":"5813_CR47","doi-asserted-by":"crossref","unstructured":"Villegas M, Romero V, S\u00e1nchez JA (2015) On the modification of binarization algorithms to retain grayscale information for handwritten text recognition. In: Iberian conference on pattern recognition and image analysis. pp 208\u2013215, Springer, Berlin","DOI":"10.1007\/978-3-319-19390-8_24"},{"key":"5813_CR48","doi-asserted-by":"crossref","unstructured":"Wang P, Sun R, Zhao H, Yu K (2013) A new word language model evaluation metric for character based languages. In: Chinese computational linguistics and natural language processing based on naturally annotated big data. pp 315\u2013324. Springer, Berlin","DOI":"10.1007\/978-3-642-41491-6_29"},{"key":"5813_CR49","unstructured":"Bluche T (2015) Deep neural networks for large vocabulary handwritten text recognition. PhD thesis, Universit\u00e9 Paris Sud-Paris XI"},{"key":"5813_CR50","unstructured":"Jean S, Cho K, Memisevic R, Bengio Y (2014) On Using Very Large Target Vocabulary for Neural Machine Translation. ArXiv e-prints 1604:032867"},{"key":"5813_CR51","unstructured":"Graves A, Schmidhuber J (2009) Offline handwriting recognition with multidimensional recurrent neural networks. In: Advances in neural information processing systems, pp 545\u2013552"},{"key":"5813_CR52","doi-asserted-by":"crossref","unstructured":"Michael J, Labahn R, Gr\u00fcning T, Z\u00f6llner J (2019) Evaluating sequence-to-sequence models for handwritten text recognition. In: 2019 International conference on document analysis and recognition (ICDAR), IEEE, pp 1286\u20131293","DOI":"10.1109\/ICDAR.2019.00208"},{"key":"5813_CR53","doi-asserted-by":"crossref","unstructured":"Voigtlaender P, Doetsch P, Ney H (2016) Handwriting recognition with large multidimensional long short-term memory recurrent neural networks. In: Frontiers in handwriting recognition (ICFHR), 2016 15th international conference on, IEEE, pp 228\u2013233","DOI":"10.1109\/ICFHR.2016.0052"},{"key":"5813_CR54","doi-asserted-by":"crossref","unstructured":"Castro D, Bezerra BL, Valen\u00e7a M (2018) 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","DOI":"10.1109\/ICFHR-2018.2018.00031"},{"key":"5813_CR55","unstructured":"Bluche T (2016) Joint line segmentation and transcription for end-to-end handwritten paragraph recognition. In: Advances in neural information processing systems, pp 838\u2013846"},{"key":"5813_CR56","doi-asserted-by":"crossref","unstructured":"Doetsch P, Kozielski M, Ney H (2014) Fast and robust training of recurrent neural networks for offline handwriting recognition. In: Frontiers in handwriting recognition (ICFHR), 2014 14th international conference on, IEEE, pp 279\u2013284","DOI":"10.1109\/ICFHR.2014.54"},{"key":"5813_CR57","doi-asserted-by":"crossref","unstructured":"Voigtlaender P, Doetsch P, Wiesler S, Schl\u00fcter R, Ney H (2015) Sequence-discriminative training of recurrent neural networks. In: Acoustics, speech and signal processing (ICASSP), 2015 IEEE International Conference on, IEEE, pp 2100\u20132104","DOI":"10.1109\/ICASSP.2015.7178341"},{"key":"5813_CR58","unstructured":"Coquenet D, Chatelain C, Paquet T (2020) End-to-end handwritten paragraph text recognition using a vertical attention network. ArXiv e-prints 1604:032868"},{"key":"5813_CR59","unstructured":"Kozielski M, Doetsch P, Ney H (2013) Improvements in RWTH\u2019s system for off-line handwriting recognition. In: 2013 12th International conference on document analysis and recognition, IEEE, pp 935\u2013939"},{"key":"5813_CR60","doi-asserted-by":"crossref","unstructured":"Pham V, Bluche T, Kermorvant C, Louradour J (2014) Dropout improves recurrent neural networks for handwriting recognition. In: Frontiers in handwriting recognition (ICFHR), 2014 14th international conference on, IEEE, pp 285\u2013290","DOI":"10.1109\/ICFHR.2014.55"},{"key":"5813_CR61","doi-asserted-by":"crossref","unstructured":"Dutta K, Krishnan P, Mathew M, Jawahar C (2018) Improving CNN-RNN hybrid networks for handwriting recognition. In: 2018 16th International conference on frontiers in handwriting recognition (ICFHR), IEEE, pp 80\u201385","DOI":"10.1109\/ICFHR-2018.2018.00023"},{"key":"5813_CR62","doi-asserted-by":"publisher","first-page":"341","DOI":"10.2991\/ijcis.d.200316.001","volume":"13","author":"X Huang","year":"2020","unstructured":"Huang X, Qiao L, Yu W, Li J, Ma Y (2020) End-to-end sequence labeling via convolutional recurrent neural network with a connectionist temporal classification layer. Int J Comput Intell Syst 13:341\u2013351. 1604:032869","journal-title":"Int J Comput Intell Syst"},{"key":"5813_CR63","doi-asserted-by":"crossref","unstructured":"Kozielski M, Rybach D, Hahn S, Schl\u00fcter R, Ney H (2013) Open vocabulary handwriting recognition using combined word-level and character-level language models. In: Acoustics, speech and signal processing (ICASSP), 2013 IEEE international conference on, IEEE, pp 8257\u20138261","DOI":"10.1109\/ICASSP.2013.6639275"},{"key":"5813_CR64","doi-asserted-by":"crossref","unstructured":"Krishnan P, Dutta K, Jawahar C (2018) Word spotting and recognition using deep embedding. In: 2018 13th IAPR international workshop on document analysis systems (DAS), IEEE, pp 1\u20136","DOI":"10.1109\/DAS.2018.70"},{"issue":"4","key":"5813_CR65","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1109\/TPAMI.2010.141","volume":"33","author":"S Espa\u00f1a-Boquera","year":"2011","unstructured":"Espa\u00f1a-Boquera S, Castro-Bleda MJ, Gorbe-Moya J, Zamora-Martinez F (2011) Improving offline handwritten text recognition with hybrid hmm\/ann models. IEEE Trans Pattern Anal Mach Intell 33(4):767\u2013779. 1312:17370","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5813_CR66","doi-asserted-by":"crossref","unstructured":"Chen Z, Wu Y, Yin F, Liu CL (2017) Simultaneous script identification and handwriting recognition via multi-task learning of recurrent neural networks. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), IEEE, vol\u00a01, pp 525\u2013530","DOI":"10.1109\/ICDAR.2017.92"},{"key":"5813_CR67","doi-asserted-by":"crossref","unstructured":"Dreuw P, Doetsch P, Plahl C, Ney H (2011) Hierarchical hybrid MLP\/HMM or rather MLP features for a discriminatively trained Gaussian HMM: a comparison for offline handwriting recognition. In: 2011 18th IEEE international conference on image processing, IEEE, pp 3541\u20133544","DOI":"10.1109\/ICIP.2011.6116480"},{"key":"5813_CR68","doi-asserted-by":"crossref","unstructured":"Doetsch P, Zeyer A, Ney H (2016) Bidirectional decoder networks for attention-based end-to-end offline handwriting recognition. In: 2016 15th international conference on frontiers in handwriting recognition (ICFHR), IEEE, pp 361\u2013366","DOI":"10.1109\/ICFHR.2016.0074"},{"key":"5813_CR69","doi-asserted-by":"crossref","unstructured":"Menasri F, Louradour J, Bianne-Bernard AL, Kermorvant C (2012) The a2ia french handwriting recognition system at the rimes-icdar2011 competition. In: Document recognition and retrieval XIX, international society for optics and photonics, vol 8297, p 82970Y","DOI":"10.1117\/12.911981"},{"key":"5813_CR70","unstructured":"Soullard Y, Ruffino C, Paquet T (2019) CTCModel: a Keras model for connectionist temporal classification. ArXiv e-prints 1312:17371"},{"key":"5813_CR71","unstructured":"Kim Y, Denton C, Hoang L, Rush AM (2017) Structured attention networks. ArXiv e-prints 1312:17372"},{"key":"5813_CR72","unstructured":"Veit A, Matera T, Neumann L, Matas J, Belongie S (2016) COCO-Text: dataset and benchmark for text detection and recognition in natural images. ArXiv e-prints 1312:17373"},{"issue":"1","key":"5813_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-015-0823-z","volume":"116","author":"M Jaderberg","year":"2016","unstructured":"Jaderberg M, Simonyan K, Vedaldi A, Zisserman A (2016) Reading text in the wild with convolutional neural networks. Int J Comput Vision 116(1):1\u201320","journal-title":"Int J Comput Vision"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05813-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05813-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05813-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T07:24:34Z","timestamp":1625729074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05813-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,27]]},"references-count":73,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["5813"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05813-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,27]]},"assertion":[{"value":"6 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Poulos acknowledges support of the National Science Foundation Graduate Research Fellowship under Grant DGE-1106400, and the National Science Foundation under Grant DMS-1638521 to the Statistical and Applied Mathematical Sciences Institute. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"Implementation code is available at the repository: .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"The authors declare no conflicts of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}