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J. Imaging 2018,4, 15 16.5% 17.0% 17.5% 18.0% 18.5% 19.0% 19.5% 20.0% 20.5% 21.0% 21.5% Word 3-gram LM Character 10-gram LM Open voc. Open voc. with val. Closed voc. Closed voc. with val. Figure11.CERresultsobtainedbythebestword-basedHMMsystemandthebest character-based HMMsystemwithopenandclosedvocabulary,withandwithoutusing thevalidationsamples for trainingtheLM. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Word 3-gram LM Character 10-gram LM Open voc. Open voc. with val. Closed voc. Closed voc. with val. Figure12.RecognitionaccuracyrateforOOVwordsbythebestword-basedHMMsystemandthebest character-basedHMMsystemwithopenandclosedvocabulary,withandwithoutusing thevalidation samples for trainingtheLM. 37% 38% 39% 40% 41% 42% 43% 44% Word 3-gram LM Character 10-gram LM Open voc. Open voc. with val. Closed voc. Closed voc. with val. Figure13.WERresultsobtainedbythebestword-basedHMMsystemandthebestcharacter-based HMMsystemwithopenandclosedvocabulary,withandwithoutusing thevalidationsamples for trainingtheLM. 141
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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
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Document Image Processing