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1、Attention-basedExtractionofStructuredInformationfromStreetViewImageryZbigniewWojnaAlexGorbanyDar-ShyangLeeyKevinMurphyyQianYuyYeqingLiyJulianIbarzyUniversityCollegeLondonyGoogleInc.Abstract—Wepresentaneuralnetworkmodel—basedonFinally,westudytheaccuracyandspeedofusin
2、g3differ-CNNs,RNNsandanovelattentionmechanism—whichachievesentCNN-basedfeatureextractors(namelyinception-v2[9],84.2%accuracyonthechallengingFrenchStreetNameSignsinception-v3[10]andinception-resnet-v2[10])asinputto(FSNS)dataset,signi?cantlyoutperformingthepreviousstate
3、ourattentionmodel.We?ndthatinception-v3andinception-oftheart(Smith’16),whichachieved72.46%.Furthermore,ournewmethodismuchsimplerandmoregeneralthantheresnet-v2performcomparably,andbothsigni?cantlyoutper-previousapproach.Todemonstratethegeneralityofourmodel,forminceptio
4、n-v2.Motivatedbytheneedforspeed,wealsoweshowthatitalsoperformswellonanevenmorechallengingstudytheeffectofusing“ablated”versionsofthesemodels,datasetderivedfromGoogleStreetView,inwhichthegoaliswhichusefewerlayers.Interestingly,we?ndthatforallthreetoextractbusinessnames
5、fromstorefronts.Finally,westudynetworks,theaccuracyinitiallyincreaseswithdepth,butthenthespeed/accuracytradeoffthatresultsfromusingCNNfeatureextractorsofdifferentdepths.Surprisingly,we?ndthatdeeperstartstodecrease.Thisisincontrasttomodelstrainedontheisnotalwaysbetter(
6、intermsofaccuracy,aswellasspeed).ILSVRCImagenetdataset[11],whichiscomparableinsizeOurresultingmodelissimple,accurateandfast,allowingittoFSNS.Forimageclassi?cation,accuracytendstoincreasetobeusedatscaleonavarietyofchallengingreal-worldtextwithdepthmonotonically.Webelie
7、vethedifferenceisthatextractionproblems.imageclassi?cationneedsverycomplicatedfeatures,whichI.INTRODUCTIONarespatiallyinvariant,whereas,fortextextraction,ithurtstoTextrecognitioninanunconstrainednaturalenvironmentisusetousesuchfeatures.achallengingcomputervisionandmac
8、hinelearningproblem.Insummary,ourcontributionsareasfollows:(1)WepresentTraditionalOpticalCharacterRecognition(OCR)systemsano
