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J. Imaging 2018,4, 15 6. Xie,Z.;Sun,Z.; Jin,L.;Feng,Z.;Zhang,S. Fullyconvolutional recurrentnetworkforhandwrittenChinese textrecognition. InProceedingsofthe 23rdInternationalConferenceonPatternRecognition(ICPR),Cancun, Mexico,4–8December2016;pp. 4011–4016. 7. Bluche,T.;Messina,R. GatedConvolutionalRecurrentNeuralNetworks forMultilingualHandwriting Recognition. InProceedingsof the13thInternationalConferenceonDocumentAnalysisandRecognition (ICDAR),Kyoto, Japan,13–15November2017. 8. Sudholt,S.;Fink,G.A. PHOCNet:Adeepconvolutionalneuralnetworkforwordspotting inhandwritten documents. InProceedingsof the 15thInternationalConferenceonFrontiers inHandwritingRecognition (ICFHR),Shenzhen,China,23–26October2016;pp. 277–282. 9. Brakensiek,A.;Rottland, J.;Kosmala,A.;Rigoll,G. Off-linehandwritingrecognitionusingvarioushybrid modeling techniquesandcharactern-grams. InProceedingsof the7th InternationalWorkshoponFrontiers inHandwrittenRecognition,Amsterdam,TheNetherlands,11–13September2000;pp. 343–352. 10. Fischer,A.; Frinken,V.; Bunke,H.; Suen,C.Y. Improvinghmm-basedkeyword spottingwith character languagemodels. In Proceedings of the 12th International Conference onDocument Analysis and Recognition(ICDAR),Washington,DC,USA,25–28August2013;pp. 506–510. 11. Santoro,A.;Parziale,A.;Marcelli,A.AHumanintheLoopApproachtoHistoricalHandwrittenDocuments Transcription. InProceedingsof the 15th InternationalConferenceonFrontiers inHandwritingRecognition (ICFHR),Shenzhen,China,23–26October2016;pp. 222–227. 12. Stefano, C.D.; Marcelli, A.; Parziale, A.; Senatore, R. Reading CursiveHandwriting. In Proceedings of the 12th International Conference on Frontiers in Handwriting Recognition, Kolkata, India, 16–18November2010;pp. 95–100. 13. Oprean, C.; Likforman-Sulem, L.; Popescu, A.; Mokbel, C. Handwrittenword recognition usingWeb resourcesandrecurrentneuralnetworks. Int. J.Doc.Anal. Recognit. (IJDAR)2015,18, 287–301. 14. Frinken,V.;Fischer,A.;MartĂ­nez-Hinarejos,C.D.Handwritingrecognition inhistoricaldocumentsusing very largevocabularies. InProceedingsof the2ndInternationalWorkshoponHistoricalDocument Imaging andProcessing,Washington,DC,USA,24August2013;pp. 67–72. 15. Swaileh, W.; Paquet, T. Handwriting RecognitionwithMulti-gram languagemodels. In Proceedings of the 14h International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan, 10–15November2017. 16. Kozielski, M.; Rybach, D.; Hahn, S.; SchlĂŒter, R.; Ney, H. Open vocabulary handwriting recognition using combined word-level and character-level language models. In Proceedings of the 2013 InternationalConferenceonAcoustics,SpeechandSignalProcessing(ICASSP’13),Vancouver,BC,Canada, 26–31May2013;pp. 8257–8261. 17. Messina, R.; Kermorvant, C. Over-generative ïŹnite state transducer n-gram for out-of-vocabulary wordrecognition. InProceedingsof the 11th IAPRInternationalWorkshoponDocumentAnalysisSystems (DAS),Tours,France,7–10April2014;pp. 212–216. 18. Shi,B.;Bai,X.;Yao,C.Anend-to-endtrainableneuralnetworkfor image-basedsequencerecognitionandits applicationtoscene text recognition. IEEETrans. PatternAnal.Mach. Intell. 2017,39, 2298–2304. 19. Serrano,N.;Castro,F.; Juan,A. TheRODRIGODatabase. InProceedingsof the7thInternationalConference onLanguageResourcesandEvaluation(LREC),Valletta,Malta,17–23May2010;pp. 2709–2712. 20. PatternRecognitionandHumanLanguageTechnology(PRHLT)ResearchCenter.2018.Availableonline: https://www.prhlt.upv.es (accessedon5January2018). 21. Fischer, A. Handwriting Recognition in Historical Documents. Ph.D. Thesis, University of Bern, Bern,Switzerland,2012. 22. Michel, J.B.; Shen, Y.K.; Aiden, A.P.; Veres, A.; Gray, M.K.; Brockman, W.; Team, T.G.B.; Pickett, J.P.; Hoiberg,D.;Clancy,D.; et al. Quantitativeanalysisof cultureusingmillionsofdigitizedbooks. Science 2010,331, 176–182. 23. Pastor,M.;Toselli,A.H.;Vidal,E. ProjectionproïŹlebasedalgorithmforslant removal. InLectureNotes in ComputerScience,Proceedingsof the InternationalConferenceon ImageAnalysis andRecognition (ICIAR’04),Porto, Portugal, 29September–1October2004; Springer: Berlin,Germany,2004;Volume3212,pp. 183–190. 147
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