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J. Imaging 2018,4, 6
of1024. Table1showstheclusteringaccuracyrateof the testedwordsusingthe three implemented
featureswhenusingvaryingnumberofclusters fromoneto10.
Table1.Clusteringaccuracyrate (percent)ofSimplifiedArabic fontvs. numberofclustersusing three
features (codebooksize=1024, lexicon 356,000).
Features NumberofCoefficients Top1 Top2 Top3 Top4 Top5 Top6 Top7 Top8 Top9 Top10
DCT 160 84.7 96.0 98.4 98.9 99.1 99.4 99.5 99.6 99.7 99.7
DCT_4B 160 78.5 91.9 96.2 97.8 98.7 99.2 99.4 99.6 99.7 99.7
DCT+DCT_4B 200 86.1 96.2 98.5 99.1 99.3 99.6 99.7 99.8 99.8 99.8
TheresultsofTable1showthat theDCT+DCT_4Bfeature isbetter thantheothertwo.Thishybrid
featurebenefitedfromthe localandglobal featureof theDCT,so itachievedgoodresults, especially in
thenoisydata. Figure4showstherelationbetweencodebooksizeandclusteringaccuracyrate.
Figure4.ClusteringaccuracyrateofSimplifiedArabic fontvs. codebooksizenumberusingDCT+
DCT_4Bfeature fordifferent topclusters.
As shown in Figure 4, the clustering accuracy rate increases when using larger number of
top-nclusterswhich isa logical consequence.Whenusingasmallnumberof clusters, eachcluster
contains largenumberofwordswhichraises thepossibilityoffindingthe testedwordwithinoneof
theseclusters.Whenthenumberofclusters increase, thenumberofwords ineachclusterdecrease,
whichreduces theclusteringaccuracyratebutat thesametimethewordswithineachclusterbecomes
moresimilar,whichstartsagain toraise theclusteringaccuracyrateevenupto thehighest levelwhen
eachclustercontainsonlyoneword.
5. LanguageRescoring
Toenhance therecognitionaccuracy, the top-hypotheses fromtheholistic recognitionresultsare
rescoredusinga languagemodel. Inoursystem,weuseda4-gramlanguagemodel thatwas trained
fromaGiga-wordArabic trainingdatabase [20]. The topn-hypotheses foreachwordarecombinedin
a lattice formatasshowninFigure5, thenweusedtheA*search technique tosearch for thebest score
path in that latticeusingthe4-gramlanguagemodel toselect thebestmatchingsentenceaccordingto
theArabic languageconstraints [21].
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book Document Image Processing"
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