This document discusses leveraging clustering techniques for document layout analysis in machine learning projects. It describes how clustering can help group similar documents based on their layout characteristics in order to identify clusters of documents that challenge models. Targeted data augmentation can then selectively augment training data from underperforming clusters to improve model performance on diverse document layouts through iterative training. Experiments showed that including documents from a specific cluster improved model performance for an object detection task compared to excluding them.

1. blog.md 6/29/2023
1 / 5
ByNikhilKumarMarepally
LeveragingClusteringforDocumentLayoutAnalysisin
MachineLearningProjects
Introduction
Inmachinelearningprojects,dealingwithdiversedocumentlayoutscanposeasignificantchallenge.
However,byleveragingclusteringtechniques,wecanidentifydocumentswithsimilarlayoutsand
selectivelyaugmentthetrainingdatatoimprovemodelperformance.Inthisblogpost,wewillexplorethe
conceptofdocumentlayoutanalysis,theimportanceoftargeteddataaugmentation,andhowclustering
canaidinidentifyingunderperformingdocumentsforaugmentation.
DocumentLayoutAnalysis
Documentlayoutanalysisinvolvesunderstandingthestructureandorganizationofdifferenttypesof
documents.Itplaysacrucialroleintaskssuchasopticalcharacterrecognition(OCR),information
extraction,anddocumentclassification.Variationindocumentlayoutscanposedifficultieswhentraining
machinelearningmodels,astheymayrequiredifferentpreprocessingforfeatureextractiontechniques.
TargetedDataAugmentation
Inscenarioswhereatrainedmodelisunderperforming,itisnotalwaysfeasibleorefficienttoinclude
randomdocumentsforadditionaltraining.Targeteddataaugmentationfocusesonselectivelyaugmenting

2. blog.md 6/29/2023
2 / 5
thetrainingdatawithsamplesthatspecificallyaddressthemodel'sweaknesses.Byaugmentingonlythe
relevantdocuments,wecanimprovethemodel'sperformancewithoutintroducingunnecessarynoise.
UtilizingClusteringforDocumentLayoutAnalysis
Clusteringtechniquesprovideavaluableapproachforgroupingsimilardocumentsbasedontheirlayout
characteristics.Thesetechniquesenableustoidentifyclustersofdocumentswithsimilarstructures,
formatting,orvisualfeatures.Byapplyingclusteringalgorithmstotheexistingdataset,wecan
automaticallygroupdocumentsintodistinctclusters,eachrepresentingaspecificlayouttype.
ProcessOverview
Hereisanoverviewoftheprocessfollowedintheproject:
.ImageFeatureExtraction:
TheVGG16modelwasusedtoextractmeaningfulfeaturesfromthedocumentimages.VGG16isa
populardeeplearningmodelthathasbeenpre-trainedonalargedatasetandcaneffectivelyextract
high-levelfeaturesfromimages.
.ClusteringUsingK-means:
TheextractedimagefeatureswerethenusedasinputfortheK-meansclusteringalgorithm.K-means
isanunsupervisedlearningalgorithmthatgroupssimilardatapointsintoclustersbasedontheir
featuresimilarity.
ByapplyingK-meansclusteringtotheimagefeatures,documentswithsimilarlayoutsweregrouped
together,formingdistinctclusters.
.DeterminingOptimalNumberofClusters:
Tofindtheoptimalnumberofclusters,anelbowplotanalysiswasperformed.Theelbowplothelps
identifythenumberofclustersthatprovidesthemostsignificantimprovementinwithin-cluster
similaritywhileavoidingexcessivefragmentation.
Theelbowplottypicallydisplaysthenumberofclustersonthex-axisandameasureofwithin-cluster
variance(suchasthesumofsquareddistances)onthey-axis.The"elbow"pointontheplot
indicatesthenumberofclusterswheretheadditionalbenefitofaddingmoreclustersbecomes
marginal.

3. blog.md 6/29/2023
3 / 5
SelectiveDataAugmentationProcess
Oncethedocumentsareclustered,wecanfocusontheclusterswherethemodelisunderperforming.By
analyzingthemisclassifiedorlow-performingsampleswithintheseclusters,wegaininsightsintothe
particulardocumentlayoutsthatchallengethemodel.Withthisknowledge,wecandesigntargeteddata
augmentationstrategiestoaddresstheweaknessesidentifiedintheunderperformingclusters.Below
imageshowsthedocumentsfromasinglecluster.
TechniquesforDataAugmentation
Dependingonthespecificrequirementsandcharacteristicsoftheunderperformingclusters,variousdata
augmentationtechniquescanbeemployed.Thesemayincludetechniquessuchasgeometric
transformations,textperturbation,imagemanipulation,orlayoutmodification.Byaugmentingthedata

4. blog.md 6/29/2023
4 / 5
withintherelevantclusters,wecanprovidethemodelwithadditionaltrainingsamplesthatresemblethe
challengingdocumentsitstruggleswith.
Experiments
Iappliedaclusteringapproachtogroupsimilardocumentsbasedontheirlayoutforasingleclassobject
detectiontask.Toevaluatetheeffectivenessofthisapproach,Iconductedtwoexperimentscomparingthe
model'sperformancewithandwithoutincludingdocumentsfromaspecificcluster(cluster6).
Theresultsshowedasignificantdifferenceinperformancebetweenthetwoscenarios.Themodeltrained
withtheinclusionofdocumentsfromcluster6outperformedthemodeltrainedwithoutthem.This
highlightstheimportanceofconsideringlayoutsimilarityintrainingthemodelforbetterobjectdetection
results.
Inaproductionenvironment,it'schallengingtopredicttheincomingtrafficandthetypesofdocumentsthat
willbeencountered.Toaddressthisuncertainty,Iemployedasystematicapproachofbinningthe
documentsbasedonlayoutsimilarity.Bysystematicallyselectingdocumentsfromdifferentclusters,the
modelbecomesmorerobustandadaptabletovariousdocumentlayouts,enablingbetterperformanceeven
withunpredictabletraffic.
Thisclusteringanddocumentselectionstrategyprovidesapracticalsolutionforhandlingdiverse
documentlayoutsandensuresthemodel'sreliabilityinreal-worldscenarios.
IterativeImprovement
Theiterativenatureofthisapproachallowsforcontinuousimprovementofthemodel'sperformance.By
evaluatingthemodel'sperformanceontheaugmenteddataandretrainingit,wecaniterativelyrefinethe
model'sabilitytohandlediversedocumentlayouts.Thisprocessensuresthatthemodelbecomesmore
robustandaccurateovertime.
Conclusion
Inmachinelearningprojectsinvolvingdiversedocumentlayouts,targeteddataaugmentationisapowerful
techniquetoenhancemodelperformance.Byleveragingclusteringalgorithms,wecanidentifyclustersof
documentswithsimilarlayouts,enablingustoselectivelyaugmentthetrainingdatawithrelevantsamples.

5. blog.md 6/29/2023
5 / 5
Thisapproachsignificantlyimprovesthemodel'sabilitytohandlevariousdocumentstructuresandleadsto
betteroverallperformance.
Documentlayoutanalysis,combinedwithtargeteddataaugmentation,providesapracticalandefficient
strategytoaddressunderperformingmodelsinspecificclassificationtasks.Byiterativelyrefiningthemodel
usingtheaugmenteddata,wecanachievehigheraccuracyandrobustnessinhandlingdiversedocument
layouts.
Wehopethisblogposthasprovidedvaluableinsightsintotheimportanceofdocumentlayoutanalysis,
targeteddataaugmentation,andtheroleofclusteringinidentifyingunderperformingdocuments.Stay
tunedformoreexcitingtopicsinthefieldofmachinelearninganddataanalysis!