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J. Imaging 2018,4, 57 Table3.ClassificationresultsusingvariousthresholdsforRULBP.Theclassificationaccuracygradually increasesandattainsamaximumata thof105units. FeatureDimension Threshold(th) Precision Recall F-Measure Accuracyin% 5 0.915 0.915 0.914 91.45 25 0.915 0.915 0.915 91.52 45 0.916 0.916 0.915 91.61 59 65 0.917 0.917 0.917 91.72 85 0.919 0.918 0.918 91.84 105 0.920 0.920 0.919 91.96 115 0.919 0.918 0.918 91.84 Figure10.Graphical comparisonof theperformancesofdifferentLBPvariants inclassifyingthe texts andnon-textspresent inhandwrittendocument images. Figure11.Graphical comparisonof theperformancesofRULBPwithdifferent threshold inclassifying the textsandnon-textspresent inhandwrittendocument images. 56
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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
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Document Image Processing