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J. Imaging 2018,4, 80 Inmoredetail, thedetectionalgorithm[9]consistsof thesteps: 1. Thewordimageisartificiallyslantedtoboth, leftandright,underdifferentslantdetectionangles. Themaximumslantangle isapproximately45degreesandtheslantanglestepdependsonthe heightof the text image. 2. Foreachof theextractedwordimages, theverticalprojectionprofile iscalculated. 3. TheWVDiscalculatedforall theaboveprojectedprofiles. 4. Thecurvesofmaximumintensityof theWVDsareextracted, justbykeepingthemaximumvalue ofeachcurveof thespace-frequencydistribution, for thespecificslant. 5. Thecurveofmaximumintensitywith thegreatestpeak,correspondingto theprojectedprofile with themost intensealternations is selected. 6. Thecorrespondingwordimage isselectedas themostnon-slantedword. Theaboveprocedure is repeatedtwice,once forabigstepsizeof10degrees (BigStep)where the areaaroundanANGLE1isselectedcloser to theslantandthesecondtimeforasmaller stepsizeof1 degree,whereamoredetaileddetection isperformedandamoreexactareaANGLE2isdetected. Thisway, the computational cost is reduced, since the first detection is performed between fewerpossibleangles inorder to localizeroughly theareaANGLE1,beforeamoreaccuratedetection (ANGLE2) isperformed in this specific area for a step sizeof onedegree. Aslant of less thanone degree isnotconsidered importantenoughtobeexamined. Finally, thedetectedangle (Detected_Slant) isgivenby Detected_Slant=ANGLE1×BigStep+ANGLE2×1 (2) 2.2. ProposedSlantRemovalTechnique Theproposedtechnique isbasedontheslantdetectionalgorithmpresentedinSection2.1,but inourcase, it isapplied to text fragments insteadofwords (Figure4). It isbasedonthe fact that in historical documents there is auniformslant that extends throughout the entiredocument image. Sincenosegmentation isperformed, fragmentsof textareusedinsteadofwords. /RFDOL]DWLRQ RI IUDJPHQWV +[: 6ODQW 'HWHFWLRQ 'RFXPHQW 6ODQW 5HPRYDO 'RFXPHQW ,PDJH 'HVODQWHG 7H[W ,PDJH Figure4.Theproposedslant removal techniqueappliedto fragmentsof textcorrects theentirepage withoutsegmentation. 33
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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