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Document Image Processing
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J. Imaging 2018,4, 43 Figure12.Balinesemanuscriptwithbinarizedgroundtruth image. Figure13.Sundanesemanuscriptwithbinarizedgroundtruth image. 4.1.2. EvaluationMethod Followingourpreviouswork[24]andtheevaluationmethodfromtheICFHRcompetition[25], threemetricsofbinarizationevaluationthatwereusedin theDIBCO2009contest [37]areused in the binarization taskevaluation for thiswork. Those threemetricsareF-Measure (FM)(Equation(3)),Peak SNR (PSNR) (Equation(5)), andNegativeRateMetric (NRM)(Equation(8)). F-Measure (FM):FMisdefinedfromRecallandPrecision. Recall= TP FN+TP ×100 (1) Precision= TP FP+TP ×100 (2) TP, definedas truepositive, occurswhen the imagepixel is labeledas foregroundand theground truth isalso.FP,definedas falsepositive,occurswhenthe imagepixel is labeledas foregroundbut the groundtruth is labeledasbackground.FN,definedas falsenegative,occurswhenthe imagepixel is labeledasbackgroundbut thegroundtruth is labeledas foreground(Equations (1)and(2)). FM= 2×Recall×Precision Recall+Precision (3) AhigherF-measure indicatesabettermatch. PeakSNR(PSNR):PSNRiscalculatedfromMeanSquareError (MSE) (Equation(4)). MSE= M ∑ x=1 N ∑ y=1 (I1(x,y)− I2(x,y))2 M∗N (4) PSNR=10× log10( C2 MSE ), (5) whereC isdefinedas1, thedifferencebetween foregroundandbackgroundcolors in thecaseof a binary image.AhigherPSNRindicatesabettermatch. 113
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Document Image Processing
Title
Document Image Processing
Authors
Ergina Kavallieratou
Laurence Likforman-Sulem
Editor
MDPI
Location
Basel
Date
2018
Language
German
License
CC BY-NC-ND 4.0
ISBN
978-3-03897-106-1
Size
17.0 x 24.4 cm
Pages
216
Keywords
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
Category
Informatik
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Document Image Processing