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J. Imaging 2018,4, 57 followed by details of the parameter values used by the classifiers. At the end, we present the evaluationmetricsusedin theexperiment. 4.1.DatabasePreparation It is foundthat theunavailabilityofastandarddatabasemaybeoneof thepossiblereasons for slowprogress insomeresearchareas, suchas text/non-text separationfromhandwrittendocuments in spite of their importance. Keeping this fact inmind, in thepresentwork, a database has been developedthatconsistsof104handwrittenengineering labcopiesandclassnotescollectedfroman engineering college. These copies include textual contents alongwithavaryingnumberof tables, graphiccomponentsandsomeprintedtexts.Alloftheselabcopiesarewrittenbymorethan20students fromdifferentengineeringandsciencestreams. Theageof thewritersvary from18to24. Pleasenote thatall thesecopiesarewritteneither inEnglishorBangla. Thecollecteddocumentsarescannedin 300DPI(Dotsper inch)usingaflatbedscannerandthenthesescannedcopiesarestoredas24bit ’BMP’ files.Asample imagefromthecurrentdatabase is showninFigure7aandthecorrespondingground truthimageisshowninFigure7b. Inthiswork, fromthose104handwrittenpages,atotalof66,058CCs areextracted,outofwhich25,011are textcomponentsand41,047arenon-textcomponents. (a) (b) Figure7. (a) sample imagefromourdataset; (b)groundtruthof thegiven image(here, redrepresents textandbluerepresentsnon-textcomponents). 4.2. Classifiers Forclassificationof theextractedCCs,fivewell-knownclassifiersareused in thiswork,namely, NaïveBayes (NB),Multi-layerperceptron (MLP),K-nearestneighbor (K-NN),Randomforest (RF) andSupportVectorMachine (SVM). In thecurrentexperimental setup,performancesofSimpleLBP, ILBP,RILBP,ULBPandRIULBPdescriptorswith eachof the consideredclassifiers for thepresent datasetaremeasured. Then, theclassifier thatperformsbetter inallormost cases isused to justify thenewlyhypothesized ’uniformpattern’ inRLBPi.e.,RULBP. It is tobenoted thatoneof thekey parametersofRULBPis thwhosevalue is subjective to thedocument image.Here,different trial runs 52
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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