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J. Imaging 2017,3, 62 Figure5.Holedegradation. (Left)original images. (Right)degradedimages. 3.4. Bleed-Through WithDocCreator it ispossible toaddbleed-throughdefects. Thisalgorithmisdirectly inspired from[49] that initiallyproposesanalgorithmforerasingthebleed-throughfromadocument image. By justgivingan input recto image, an inputverso imageand theamountofwisheddegradation, a physicalmodel is applied. Thismodelmimics the verso ink that seeps through the recto side. Thismodel simulatesananisotropicdiffusionandeachpixelat time t+1 ismodifiedaccordingto the pixelsvaluesat time twiththe followingequation: It+1i,j = I t i,j+λ∗(ctNi,j · NIti,j+ctSi,j · SIti,j+ctEi,j · EIti,j+ctWi,j · WIti,j) where I is the recto image, V is the verso image, lambda a constant value in [0;0.25] and N, S, E, W are themnemonic subscripts forNorth, South, East, West. NIti,j and ctNi,j are defined as: NIti,j =Vti−1,j− Iti,j and ctNi,j = 1 1+ (Vti−1,j−I0i,j)2 σ2 . Theuser sets thenumberof iterationsand thus the quantityofgeneratedbleed-through. SeeFigure6 forableed-throughexample. Figure6.Bleed-throughdefect. (Left)original image. (Right)degradedimage. 177
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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