车牌识别外文翻译讲解学习.docx

车牌识别外文翻译讲解学习.docx

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车牌识别外文翻译讲解学习

车牌识别外文翻译

中英文翻译

Aconfigurablemethodformulti-stylelicenseplaterecognition

Automaticlicenseplaterecognition（LPR）hasbeenapracticaltechniqueinthepastdecades.Numerousapplications,suchasautomatictollcollection,criminalpursuitandtrafficlawenforcement,havebeenbenefitedfromit.Althoughsomenoveltechniques,forexampleRFID（radiofrequencyidentification）,WSN（wirelesssensornetwork）,etc.,havebeenproposedforcarIDidentification,LPRonimagedataisstillanindispensabletechniqueincurrentintelligenttransportationsystemsforitsconvenienceandlowcost.LPRisgenerallydividedintothreesteps:

licenseplatedetection,charactersegmentationandcharacterrecognition.ThedetectionsteproughlyclassifiesLPandnon-LPregions,thesegmentationstepseparatesthesymbols/charactersfromeachotherinoneLPsothatonlyaccurateoutlineofeachimageblockofcharactersisleftfortherecognition,andtherecognitionstepfinallyconvertsgreylevelimageblockintocharacters/symbolsbypredefinedrecognitionmodels.AlthoughLPRtechniquehasalongresearchhistory,itisstilldrivenforwardbyvariousarisingdemands,themostfrequentoneofwhichisthevariationofLPstyles,forexample:

（1）Appearancevariationcausedbythechangeofimagecapturingconditions.

（2）Stylevariationfromonenationtoanother.

（3）StylevariationwhenthegovernmentreleasesnewLPformat.

Wesummedthemupintofourfactors,namelyrotationangle,linenumber,charactertypeandformat,aftercomprehensiveanalysesofmulti-styleLPcharacteristicsonrealdata.Generallyspeaking,anychangeoftheabovefourfactorscanresultinthechangeofLPstyleorappearanceandthenaffectthedetection,segmentationorrecognitionalgorithms.IfoneLPhasalargerotationangle,thesegmentationandrecognitionalgorithmsforhorizontalLPmaynotwork.IftherearemorethanonecharacterlinesinoneLP,additionallineseparationalgorithmisneededbeforeasegmentationprocess.Withthevariationofcharactertypeswhenweapplythemethodfromonenationtoanother,theabilitytore-definetherecognitionmodelsisneeded.Whatismore,thechangeofLPstylesrequiresthemethodtoadjustbyitselfsothatthesegmentedandrecognizedcharactercandidatescanmatchbestwithanLPformat.

Severalmethodshavebeenproposedformulti-nationalLPsormultiformatLPsinthepastyearswhilefewofthemcomprehensivelyaddressthestyleadaptationproblemintermsoftheabovementionedfactors.SomeofthemonlyclaimtheabilityofprocessingmultinationalLPsbyredefiningthedetectionandsegmentationrulesorrecognitionmodels.

Inthispaper,weproposeaconfigurableLPRmethodwhichisadaptablefromonestyletoanother,particularlyfromonenationtoanother,bydefiningthefourfactorsasparameters.Userscanconstrainthescopeofaparameterandatthesametimethemethodwilladjustitselfsothattherecognitioncanbefasterandmoreaccurate.SimilartoexistingLPRtechniques,wealsoprovidedetailsofdetection,segmentationandrecognitionalgorithms.ThedifferenceisthatweemphasizeontheconfigurableframeworkforLPRandtheextensibilityoftheproposedmethodformultistyleLPsinsteadoftheperformanceofeachalgorithm.

Inthepastdecades,manymethodshavebeenproposedforLPRthatcontainsdetection,segmentationandrecognitionalgorithms.Inthefollowingparagraphs,thesealgorithmsandLPRmethodsbasedonthemarebrieflyreviewed.

LPdetectionalgorithmscanbemainlyclassifiedintothreeclassesaccordingtothefeaturesused,namelyedgebasedalgorithms,colorbasedalgorithmsandtexture-basedalgorithms.ThemostcommonlyusedmethodforLPdetectioniscertainlythecombinationsofedgedetectionandmathematicalmorphology.Inthesemethods,gradient（edges）isfirstextractedfromtheimageandthenaspatialanalysisbymorphologyisappliedtoconnecttheedgesintoLPregions.AnotherwayiscountingedgesontheimagerowstofindoutregionsofdenseedgesortodescribethedenseedgesinLPregionsbyaHoughtransformation.Edgeanalysisisthemoststraightforwardmethodwithlowcomputationcomplexityandgoodextensibility.Comparedwithedgebasedalgorithms,colorbasedalgorithmsdependmoreontheapplicationconditions.SinceLPsinanationoftenhaveseveralpredefinedcolors,researchershavedefinedcolormodelstosegmentregionofinterestsastheLPregions.Thiskindofmethodcanbeaffectedalotbylightingconditions.Towinbothhighrecallandlowfalsepositiverates,textureclassificationhasbeenusedforLPdetection.InRef.Kimetal.usedanSVMtotraintextureclassifierstodetectimageblockthatcontainsLPpixels.InRef.theauthorsusedGaborfilterstoextracttexturefeaturesinmultiscalesandmultiorientationstodescribethetexturepropertiesofLPregions.InRef.ZhangusedXandYderivativefeatures,grey-valuevarianceandAdaboostclassifiertoclassifyLPandnon-LPregionsinanimage.InRefs.waveletfeatureanalysisisappliedtoidentifyLPregions.Despitethegoodperformanceofthesemethodsthecomputationcomplexitywilllimittheirusability.Inaddition,texture-basedalgorithmsmaybeaffectedbymulti-lingualfactors.

Multi-lineLPsegmentationalgorithmscanalsobeclassifiedintothreeclasses,namelyalgorithmsbasedonprojection，binarizationandglobaloptimization.Intheprojectionalgorithms,gradientorcolorprojectiononverticalorientationwillbecalculatedatfirst.The“valleys”ontheprojectionresultareregardedasthespacebetweencharactersandusedtosegmentcharactersfromeachother.SegmentedregionsarefurtherprocessedbyverticalprojectiontoobtainpreciseboundingboxesoftheLPcharacters.SincesimplesegmentationmethodsareeasilyaffectedbytherotationofLP,segmentingtheskewedLPbecomesakeyissuetobesolved.Inthebinarizationalgorithms,globalorlocalmethodsareoftenusedtoobtainforegroundfrombackgroundandthenregionconnectionoperationisusedtoobtaincharacterregions.Inthemostrecentwork,localthresholddeterminationandslidewindowtechniquearedevelopedtoimprovethesegmentationperformance.Intheglobaloptimizationalgorithms,thegoalisnottoobtaingoodsegmentationresultforindependentcharactersbuttoobtainacompromiseofcharacterspatialarrangementandsinglecharacterrecognitionresult.HiddenMarkovchainhasbeenusedtoformulatethedynamicsegmentationofcharactersinLP.Theadvantageofthealgorithmisthattheglobaloptimizationwillimprovetherobustnesstonoise.Andthedisadvantageisthatpreciseformatdefinitionisnecessarybeforeasegmentationprocess.

CharacterandsymbolrecognitionalgorithmsinLPRcanbecategorizedintolearning-basedonesandtemplatematchingones.Fortheformerone,artificialneuralnetwork（ANN）isthemostlyusedmethodsinceitisprovedtobeabletoobtainverygoodrecognitionresultgivenalargetrainingset.AnimportantfactorintraininganANNrecognitionmodelforLPistobuildreasonablenetworkstructurewithgoodfeatures.SVM-basedmethodisalsoadoptedinLPRtoobtaingoodrecognitionperformancewithevenfewtrainingsamples.Recently,cascadeclassifiermethodisalsousedforLPrecognition.Templatematchingisanotherwidelyusedalgorithm.Generally,researchersneedtobuildtemplateimagesbyhandfortheLPcharactersandsymbols.Theycanassignlargerweightsfortheimportantpoints,forexample,thecornerpoints,inthetemplatetoemphasizethedifferentcharacteristicsofthecharacters.Invarianceoffeaturepointsisalsoconsideredinthetemplatematchingmethodtoimprovetherobustness.Thedisadvantageisthatitisdifficulttodefinenewtemplatebytheuserswhohavenoprofessionalknowledgeonpatternrecognition,whichwillrestricttheapplicationofthealgorithm.

Basedontheabovementionedalgorithms,lotsofLPRmethodshavebeendeveloped.However,thesemethodsaremainlydevelopedforspecificnationorspecialLPformats.InRef.theauthorsfocusonrecognizingGreekLPsbyproposingnewsegmentationandrecognitionalgorithms.ThecharactersonLPsarealphanumericswithseveralfixedformats.InRef.Zhangetal.developedalearning-basedmethodforLPdetectionandcharacterrecognition.TheirmethodismainlyforLPsofKoreanstyles.InRef.opticalcharacterrecognition（OCR）techniqueareintegratedintoLPRtodevelopgeneralLPRmethod,whiletheperformanceofOCRmaydropwhenfacingLPsofpoorimagequalitysinceitisdifficulttodiscriminaterealcharacterfromcandidateswithoutformatsupervision.ThismethodcanonlyselectcandidatesofbestrecognitionresultsasLPcharacterswithoutrecoveryprocess.Wangetal.developedamethodtorecognizeLPRwithvariousviewingangles.Skewfactorisconsideredintheirmethod.InRef.theauthorsproposedanautomaticLPRmethodwhichcantreatthecasesofchangesofillumination,vehiclespeed,routesandbackgrounds,whichwasrealizedbydevelopingnewdetectionandsegmentationalgorithmswithrobustnesstotheilluminationandimageblurring.Theperformanceofthemethodisencouragingwhiletheauthorsdonotpresenttherecognitionresultinmultinationormultistyleconditions.InRef.theauthorsproposeanLPRmethodinmultinationalenvironmentwithcharactersegmentationandformatindependentrecognition.Sincenorecognitioninformationisusedincharactersegmentation,falsesegmentedcharactersfrombackgroundnoisemaybeproduced.Whatismore,therecognitionmethodisnotalearning-basedmethod,whichwilllimititsextensibility.InRef.Mecoccietal.proposeagenerativerecognitionmethod.Generati