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J. Imaging 2018,4, 15 24. Toselli,A.H.; Juan,A.;GonzĂĄlez, J.; Salvador, I.;Vidal,E.;Casacuberta,F.;Keysers,D.;Ney,H. Integrated HandwritingRecognitionandInterpretationusingFinite-StateModels. Int. J.PatternRecognit. Artif. Intell. 2004,18, 519–539. 25. Testhyphens–Testinghyphenationpatterns.2018.Availableonline: https://www.ctan.org/tex-archive/ macros/latex/contrib/testhyphens (accessedon5January2018) 26. Kneser, R.; Ney,H. Improvedbacking-off forM-gram languagemodeling. In Proceedings of the 1995 International Conference onAcoustics, Speech, and Signal Processing (ICASSP’95), Detroit, MI, USA, 9–12May1995;Volume1,pp. 181–184. 27. Stolcke,A. SRILM—Anextensible languagemodelingtoolkit. InProceedingsof the3rdInterspeech,Denver, CO,USA,16–20September2002;pp. 901–904. 28. Young, S.; Evermann, G.; Gales, M.; Hain, T.; Kershaw, D.; Liu, X.; Moore, G.; Odell, J.; Ollason, D.; Povey, D.; et al. The HTK Book (for HTK Version 3.4); CambridgeUniversity EngineeringDepartment: Cambridge,UK,2006. 29. LujĂĄn-Mares,M.; Tamarit,V.;Alabau,V.;MartĂ­nez-Hinarejos,C.D.; Pastor,M.; Sanchis,A.; Toselli,A.H. iATROS:ASpeech andHandwritingRecognition System. V Jornadas enTecnologĂ­as delHabla, 2008; pp.75–78.Availableonline: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.329.6708&rep= rep1&type=pdf (accessedon5January2018) 30. Hermansky,H.;Ellis,D.P.W.;Sharma,S. Tandemconnectionist featureextractionforconventionalHMM systems. In Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP’00), Istanbul,Turkey,5–9 June2000;Volume3,pp. 1635–1638. 31. Graves, A. RNNLIB: A Recurrent Neural Network Library for Sequence Learning Problems. 2016. Availableonline: http://sourceforge.net/projects/rnnl/(accessedon5January2018) 32. Chammas, E. StructuringHidden Information inMarkovModelingwithApplication toHandwriting Recognition. Ph.D.Thesis,TelecomParisTech,Paris,France,2017. 33. Simonyan,K.;Zisserman,A.Verydeepconvolutionalnetworksfor large-scale imagerecognition. arXiv2014, arXiv:1409.1556. 34. Gu, J.;Wang,Z.;Kuen, J.;Ma,L.;Shahroudy,A.;Shuai,B.;Liu,T.;Wang,X.;Wang,G. Recentadvances in convolutionalneuralnetworks. arXiv 2015, arXiv:1512.07108. 35. Graves, A.; FernĂĄndez, S.; Gomez, F.; Schmidhuber, J. Connectionist temporal classiïŹcation: labeling unsegmented sequence datawith recurrent neural networks. In Proceedings of the 23rd international conferenceonMachine learningACM,Pittsburgh,PA,USA,25–29 June2006;pp. 369–376. 36. Zeyer,A.;SchlĂŒter,R.;Ney,H. TowardsOnline-RecognitionwithDeepBidirectionalLSTMAcousticModels. InProceedingsof the2016INTERSPEECH,SanFrancisco,CA,USA,8–12September2016;pp. 3424–3428. 37. Glorot, X.; Bengio, Y. Understanding the difïŹculty of training deep feedforward neural networks. InProceedingsof theThirteenthInternationalConferenceonArtiïŹcial IntelligenceandStatistics,Sardinia, Italy,13–15May2010;pp. 249–256. 38. Kingma,D.;Ba, J. Adam:Amethodforstochasticoptimization. arXiv 2014, arXiv:1412.6980. 39. Tieleman, T.; Hinton, G. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERANeuralNetw.Mach. Learn. 2012,4, 26–31. 40. Qian,N.Onthemomentumtermingradientdescent learningalgorithms. NeuralNetw. 1999,12, 145–151. 41. Zeiler,M.D.ADADELTA:anadaptive learningratemethod. arXiv 2012, arXiv:1212.5701. 42. Ioffe,S.;Szegedy,C. Batchnormalization:Acceleratingdeepnetworktrainingbyreducinginternalcovariate shift. InProceedingsof the InternationalConferenceonMachineLearning,Lille, France, 6–11 July2015; pp.448–456. 43. Miao,Y.;Gowayyed,M.;Metze,F. EESEN:End-to-endspeechrecognitionusingdeepRNNmodelsand WFST-baseddecoding. InProceedingsof the2015IEEEWorkshoponAutomaticSpeechRecognitionand Understanding(ASRU),Scottsdale,AZ,USA,13–17December2015;pp. 167–174. 44. Levenshtein,V.I. Binarycodescapableof correctingdeletions, insertions, andreversals. Sov. Phys. Dokl. 1966,10, 707–710. 45. Knezevic, A. Overlapping ConïŹdence Intervals and Statistical SigniïŹcance; StatNews; Cornell University StatisticalConsultingUnit: Ithaca,NY,USA,2008;Volume73. 148
zurĂŒck zum  Buch Document Image Processing"
Document Image Processing
Titel
Document Image Processing
Autoren
Ergina Kavallieratou
Laurence Likforman-Sulem
Herausgeber
MDPI
Ort
Basel
Datum
2018
Sprache
deutsch
Lizenz
CC BY-NC-ND 4.0
ISBN
978-3-03897-106-1
Abmessungen
17.0 x 24.4 cm
Seiten
216
Schlagwörter
document image processing, preprocessing, binarizationl, text-line segmentation, handwriting recognition, indic/arabic/asian script, OCR, Video OCR, word spotting, retrieval, document datasets, performance evaluation, document annotation tools
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Document Image Processing