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J. Imaging 2018,4, 80 2.MaterialsandMethods 2.1. SlantDetectionAlgorithm Asalreadymentioned, in theproposedtechnique, theslantdetectionalgorithmthat ispresented indetail in [9] isused. Thereason is thatanalgorithmfor thedetection isneededandthisonehas provedtobepopularandsuccessful [3]. Thisspecificalgorithmmakesuseof thedifferencebetweentheascendersanddescenders. Incase the text isvertical, thedifference is larger (Figure3). Todetect that, ituses theWignerVilledistribution (WVD)[21], aspace-frequencydistributionofCohen’sclass,which isgivenbythe formula W(s, f)= +∞∫ −∞ z(s+τ/2)z∗(s−τ/2)e−2iπ fτdτ, (1) where s the signal, f the frequency, z(s) represents theanalytical signalassociatedwith thediscrete signalh(s), that in thepaper [9] is theverticalprojectionprofileof theword. Figure3.Fromleft toright: slantedwords, correspondinghistogramsandmaximumintensitycurves ofWVD(Wigner–Villedistribution). 32
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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