车牌识别外文翻译Word文档格式.docx

车牌识别外文翻译Word文档格式.docx

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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:

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（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.theauthorsusedGaborfilterstoextracttexturefeaturesinmultiscalesandmultiorientationstodescribethetexturepropertie