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中英文翻译Aconfigurablemethodformulti-stylelicenseplaterecognitionAutomaticlicenseplaterecognition(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.1(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.Userscanconstrainthescopeofaparameterandatthesametimethemethodwilladjustitselfsothattherecognitioncanbefasterandmore2accurate.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,textureclassificationhasbeenusedfor3LPdetection.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.Sin