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areperformedtochoose theoptimalvalueof th. In thiswork,Weka3 [28], adataminingsoftware
(UniversityofWaikato,Hamilton,NewZealand),hasbeenusedforclassiï¬cationandvisualization
purpose. Thevaluesof theclassiï¬ersâparametersused in thecurrentexperimentaregiven inTable1.
Table1.Detailvaluesof theparametersusedbytheclassiï¬ersunderconsideration.
Classiï¬er ParameterswithValues
NB â¢Batchsize:
100â¢Normaldistributionfornumericattributes
MLP â¢LearningRate for thebackpropagationalgorithm: 0.3
â¢MomentumRate: 0.2
â¢Numberofepochs to train through: 500
â¢LearningRate: 0.3
SMO â¢ComplexityconstantC:1
â¢ToleranceParameter: 1.0Ã10â3
â¢Epsilonforround-offerror: 1.0Ã10â12
â¢Therandomnumberseed: 1
K-NN â¢K:1â¢Batchsize: 100
RF â¢Batchsize: 100
â¢Minimumnumberof instancesper leaf: 1
â¢Minimumnumericclassvarianceproportionof trainvariance forsplit: 1.0Ã10â3
â¢Themaximumdepthof the tree: unlimited
4.3. PerformanceMetrics
Theperformancesof theLBPvariantsaremeasuredusingthe followingconventionalmetrics:
Recall= TP
TP+FN , (11)
Precision= TP
TP+FP , (12)
FM= 2ÃRecallÃPrecision
Recall+Precision , (13)
Accuracy= TP+TN
Total number of samples Ã100%. (14)
InEquations (11)â(14),TP,FP,TNandFN represent truepositive, falsepositive, truenegative
andfalsenegative, respectively. It is tobenotedthatall theexperimentsaredoneusing3-foldcross
validationandtheï¬nal resultsarecomputedafter takingtheaverageperformanceof the three folds.
5. ExperimentalResults
Detailed results for each LBP based feature descriptors except RULBPwith each of the ï¬ve
classiï¬ers for thecurrentdatabasearegiveninTable2. FromTable2, it canbeobservedthat theRF
classiï¬eroutperformsothers. Thus, classiï¬cationresults forRULBPwithdifferent thresholdvaluesare
computedusingRFclassiï¬eronly.Wealsosee that theRULBPoperatorgives thebest accuracy in
classiï¬cation,amongall theLBPvariantsconsidered.Detailedresultsdepictingtheperformanceof
RULBPfordifferent thresholdsaregiven inTable3.Apictorial comparisonamongtheperformances
of different LBPoperators usingRF classiï¬er is given in Figure 8. Figure 9 shows the imageof a
documentcontainingtextwritten inBanglaandclassiï¬edusingRULBP,whichgives thebest result
amongallLBPvariants. Inaddition to this, agraphical comparisonof theperformanceofvariousLBP
53
back to the
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