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J. Imaging 2018,4, 41 References 1. JĂŒrgen,S.Deeplearning inneuralnetworks:Anoverview.NeuralNetw. 2015,61, 85–117. 2. Ciregan, D.; Meier, U.; Schmidhuber, J. Multi-column deep neural networks for image classiïŹcation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence,RI,USA,16–21 June2012. 3. Krizhevsky,A.;Sutskever, I.;Hinton,G.E. ImagenetclassiïŹcationwithdeepconvolutionalneuralnetworks. In Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, 3–8December2012. 4. Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE1998,86, 2278–2324. [CrossRef] 5. Navneet,D.;Triggs,B.Histogramsoforientedgradients forhumandetection. InProceedingsof theCVPR 2005IEEEComputerSocietyConferenceonComputerVisionandPatternRecognition,SanDiego,CA,USA, 20–25 June2005;Volume1. 6. Lowe,D.G.Distinctive imagefeatures fromscale-invariantkeypoints. Int. J.Comput.Vis. 2004,60, 91–110. [CrossRef] 7. Surinta,O.;Karaaba,M.F.;Schomaker,L.R.B.;Wiering,M.A.Recognitionofhandwrittencharactersusing localgradient featuredescriptors.Eng.Appl.Artif. Intell. 2015,45, 405–414. [CrossRef] 8. Ojala,T.;PietikĂ€inen,M.;Harwood,D.Acomparativestudyof texturemeasureswithclassiïŹcationbasedon featureddistributions.PatternRecognit. 1996,29, 51–59. [CrossRef] 9. Bay,H.;Tuytelaars,T.;vanGool,L.Surf: Speededuprobust features. InProceedingsof the9thEuropean ConferenceonComputerVision,Graz,Austria,7–13May2006;pp.404–417. 10. Wang,X.;Paliwal,K.K.Featureextractionanddimensionalityreductionalgorithmsandtheirapplications in vowel recognition.PatternRecognit. 2003,36, 2429–2439. [CrossRef] 11. Zeiler,M.D.;Rob,F.Visualizingandunderstandingconvolutionalnetworks. InProceedingsof theEuropean ConferenceonComputerVision,Zurich,Switzerland,6–12September2014. 12. Simonyan,K.;Andrew,Z.Verydeepconvolutionalnetworks for large-scale imagerecognition. arXiv, 2004. 13. Jaderberg,M.;Simonyan,K.;Zisserman,A.Spatial transformernetworks. InProceedingsof theAdvances in Neural InformationProcessingSystems,Montreal,QC,Canada,11–12December2015. 14. Ciresžan, D.; Ueli, M. Multi-column deep neural networks for ofïŹ‚ine handwritten Chinese character classiïŹcation. In Proceedings of the 2015 International JointConference onNeuralNetworks (IJCNN), Killarney, Ireland,12–17 July2015. 15. Sarkhel,R.;Das,N.;Das,A.;Kundu,M.;Nasipuri,M.AMulti-scaleDeepQuadTreeBasedFeatureExtraction Methodfor theRecognitionof IsolatedHandwrittenCharactersofpopular IndicScripts.PatternRecognit. 2017,71, 78–93. [CrossRef] 16. Ahranjany, S.S.; Razzazi, F.;Ghassemian,M.H.Averyhighaccuracyhandwritten character recognition system forFarsi/Arabicdigits usingConvolutionalNeuralNetworks. InProceedingsof the 2010 IEEE Fifth InternationalConferenceonBio-InspiredComputing: TheoriesandApplications (BIC-TA),Changsha, China,23–26September2010. 17. Sethi, I.K.;Chatterjee,B.MachineRecognitionofHand-printedDevnagriNumerals. IETEJ.Res. 1976,22, 532–535. [CrossRef] 18. Sharma,N.; Pal, U.; Kimura, F.; Pal, S. Recognition of off-line handwrittendevnagari characters using quadratic classiïŹer. InComputerVision,Graphics and ImageProcessing; Springer: Berlin/Heidelberg,Germany, 2006;pp.805–816. 19. Deshpande,P.S.;Malik,L.;Arora,S.FineClassiïŹcation&RecognitionofHandWrittenDevnagariCharacters withRegularExpressions&MinimumEditDistanceMethod. JCP2008,3, 11–17. 20. Arora, S.; Bhatcharjee,D.; Nasipuri,M.;Malik, L.A two stage classiïŹcation approach for handwritten Devnagari characters. InProceedingsof the InternationalConferenceonComputational Intelligenceand MultimediaApplications,Sivakasi,TamilNadu, India,13–15December2007;Volume2,pp.399–403. 21. Arora,S.;Bhattacharjee,D.;Nasipuri,M.;Basu,D.K.;Kundu,M.;Malik,L.Studyofdifferent featureson handwrittenDevnagari character. InProceedingsof the20092ndInternationalConferenceonEmerging Trends inEngineeringandTechnology(ICETET),Nagpur, India,16–18December2009;pp.929–933. 99
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