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J. Imaging 2018,4, 15
AppendixA. SomeRecognitionExamples
ThisAppendixpresentssomerecognitionexamples. FiguresA1–A3present thebesthypothesis
obtainedforseveral linesof theRodrigocorpus in theopenvocabularyexperiments,byusinga3-gram
word-basedLM,a4-gramsub-word-basedLManda10-gramcharacter-basedLM.
Text Image
TextReference muerteepeormeresciaelporquantopassaraelmandami
Word-based1-best me&peormataraelporquantopaganaelmanda
Sub-word-based1-best mundo<SPACE>&<SPACE>por<SPACE>matara<SPACE>el<SPACE>por<SPACE>quanto<SPACE>pagana<SPACE>el<SPACE>manda<SPACE>
mundo&pormataraelporquantopaganaelmanda
Character-based1-best mucho<SPACE>&<SPACE>por<SPACE>meresc ia<SPACE>e l<SPACE>
por<SPACE>quanto<SPACE>pagaua<SPACE>e l<SPACE>mandami
mucho&pormeresciaelporquantopagauaelmandami
FigureA1.Exampleof thebesthypothesesobtainedfor the12th lineofpage500ofRodrigo.
Text Image
TextReference portugal.
Word-based1-best portugal.portugal.
Sub-word-based1-best pe
tugalzopetugalzo
Character-based1-best por
tugazportugaz
FigureA2.Exampleof thebesthypothesesobtainedfor the9th lineofpage619ofRodrigo.
Text Image
TextReference maronlocaualleroeseyendoCaualleroenfermomuymal
Word-based1-best nonloCauallero&seyendoCaualleroenfermomuydia
Sub-word-based1-best naron<SPACE> la<SPACE>Caualle
ro<SPACE>&<SPACE>seyendo<SPACE>Caualle
ro<SPACE>enfermo<SPACE>muy<SPACE>dia<SPACE>
naronlaCauallero&seyendoCaualleroenfermomuydia
Character-based1-best maron<SPACE> l a<SPACE>caua l l e ro<SPACE>&<SPACE>seyendo
<SPACE>Caual le ro<SPACE>enfermo<SPACE>muy<SPACE>mal
maronlacauallero&seyendoCaualleroenfermomuymal
FigureA3.Exampleof thebesthypothesesobtainedfor the4th lineofpage514ofRodrigo.
References
1. España-Boquera,S.;Castro-Bleda,M.J.;Gorbe-Moya, J.;Zamora-Martinez,F. ImprovingOfflineHandwritten
TextRecognitionwithHybridHMM/ANNModels. IEEETrans. PatternAnal.Mach. Intell. 2011,33, 767–779.
2. Al-Hajj-Mohamad,R.;Likforman-Sulem,L.;Mokbel,C. CombiningSlanted-FrameClassifiers for Improved
HMM-BasedArabicHandwritingRecognition. IEEETrans. PatternAnal.Mach. Intell. 2009,31, 1165–1177.
3. Vinciarelli,A. Asurveyonoff-linecursivewordrecognition. PatternRecognit. 2002,35, 1433–1446.
4. Bianne-Bernard,A.L.;Menasri,F.;El-Hajj,R.;Mokbel,C.;Kermorvant,C.;Likforman-Sulem,L. Dynamic
andContextual Information inHMMmodeling forHandwrittenWordRecognition. IEEETrans. Pattern
Anal.Mach. Intell. 2011,99, 2066–2080.
5. Graves,A. SupervisedSequenceLabellingwithRecurrentNeuralNetworks. Ph.D.Thesis, Technische
UniversitätMünchen,Munich,Germany,2008.
146
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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
- Kategorie
- Informatik