车牌识别外文翻译

1、中英文翻译Aconfigurablemethodformulti-stylelicenseplaterecognitionAutomaticlicenseplaterecognition(LPR)hasbeenapracticaltechniqueinthepastdecades.Numerousapplications,suchasautomatictollcollection,criminalpursuitandtrafficlawenforcement,havebeenbenefitedfromit.Althoughsomenoveltechniques,forexampleRFID(r

2、adiofrequencyidentification),WSN(wirelesssensornetwork),etc.,havebeenproposedforcarIDidentification,LPRonimagedataisstillanindispensabletechniqueincurrentintelligenttransportationsystemsforitsconvenienceandlowcost.LPRisgenerallydividedintothreesteps:licenseplatedetection,charactersegmentationandchar

3、acterrecognition.ThedetectionsteproughlyclassifiesLPandnon-LPregions,thesegmentationstepseparatesthesymbols/charactersfromeachotherinoneLPsothatonlyaccurateoutlineofeachimageblockofcharactersisleftfortherecognition,andtherecognitionstepfinallyconvertsgreylevelimageblockintocharacters/symbolsbypredef

4、inedrecognitionmodels.AlthoughLPRtechniquehasalongresearchhistory,itisstilldrivenforwardbyvariousarisingdemands,themostfrequentoneofwhichisthevariationofLPstyles,forexample:Appearancevariationcausedbythechangeofimagecapturingconditions.Stylevariationfromonenationtoanother.Stylevariationwhenthegovern

5、mentreleasesnewLPformat.Wesummedhemupintofourfactors,namelyrotationangle,linenumber,charactertypeandformat,aftercomprehensiveanalysesofmulti-styleLPcharacteristicsonrealdata.Generallyspeaking,anychangeoftheabovefourfactorscanresultinthechangeofLPstyleorappearanceandthenaffectthedetection,segmentatio

6、norrecognitionalgorithms.IfoneLPhasalargerotationangle,thesegmentationandrecognitionalgorithmsforhorizontalLPmaynotwork.IftherearemorethanonecharacterlinesinoneLP,additionallineseparationalgorithmisneededbeforeasegmentationprocess.Withthevariationofcharactertypeswhenweapplythemethodfromonenationtoan

7、other,theabilitytore-definetherecognitionmodelsisneeded.Whatismore,thechangeofLPstylesrequiresthemethodtoadjustbyitselfsothatthesegmentedandrecognizedcharactercandidatescanmatchbestwithanLPformat.Severalmethodshavebeenproposedformulti-nationalLPsormultiformatLPsinthepastyearswhilefewofthemcomprehens

8、ivelyaddressthestyleadaptationproblemintermsoftheabovementionedfactors.SomeofthemonlyclaimtheabilityofprocessingmultinationalLPsbyredefiningthedetectionandsegmentationrulesorrecognitionmodels.Inthispaper,weproposeaconfigurableLPRmethodwhichisadaptablefromonestyletoanother,particularlyfromonenationto

9、another,bydefiningthefourfactorsasparameters.Userscanconstrainthescopeofaparameterandatthesametimethemethodwilladjustitselfsothattherecognitioncanbefasterandmoreaccurate.SimilartoexistingLPRtechniques,wealsoprovidedetailsofdetection,segmentationandrecognitionalgorithms.Thedifferenceisthatweemphasize

10、ontheconfigurableframeworkforLPRandtheextensibilityoftheproposedmethodformultistyleLPsinsteadoftheperformanceofeachalgorithm.Inthepastdecades,manymethodshavebeenproposedforLPRthatcontainsdetection,segmentationandrecognitionalgorithms.Inthefollowingparagraphs,thesealgorithmsandLPRmethodsbasedonthemar

11、ebrieflyreviewed.LPdetectionalgorithmscanbemainlyclassifiedintothreeclassesaccordingtothefeaturesused,namelyedgebasedalgorithms,colorbasedalgorithmsandtexture-basedalgorithms.ThemostcommonlyusedmethodforLPdetectioniscertainlythecombinationsofedgedetectionandmathematicalmorphology.Inthesemethods,grad

12、ient(edges)isfirstextractedfromtheimageandthenaspatialanalysisbymorphologyisappliedtoconnecttheedgesintoLPregions.AnotherwayiscountingedgesontheimagerowstofindoutregionsofdenseedgesortodescribethedenseedgesinLPregionsbyaHoughtransformation.Edgeanalysisisthemoststraightforwardmethodwithlowcomputation

13、complexityandgoodextensibility.Comparedwithedgebasedalgorithms,colorbasedalgorithmsdependmoreontheapplicationconditions.SinceLPsinanationoftenhaveseveralpredefinedcolors,researchershavedefinedcolormodelstosegmentregionofinterestsastheLPregions.Thiskindofmethodcanbeaffectedalotbylightingconditions.To

14、winbothhighrecallandlowfalsepositiverates,textureclassificationhasbeenusedforLPdetection.InRef.Kimetal.usedanSVMtotraintextureclassifierstodetectimageblockthatcontainsLPpixels.InRef.theauthorsusedGaborfilterstoextracttexturefeaturesinmultiscalesandmultiorientationstodescribethetexturepropertiesofLPr

15、egions.InRef.ZhangusedXandYderivativefeatures,grey-valuevarianceandAdaboostclassifiertoclassifyLPandnon-LPregionsinanimage.InRefs.waveletfeatureanalysisisappliedtoidentifyLPregions.Despitethegoodperformanceofthesemethodsthecomputationcomplexitywilllimittheirusability.Inaddition,texture-basedalgorith

16、msmaybeaffectedbymulti-lingualfactors.Multi-lineLPsegmentationalgorithmscanalsobeclassifiedintothreeclasses,namelyalgorithmsbasedonprojection,binarizationandglobaloptimization.Intheprojectionalgorithms,gradientorcolorprojectiononverticalorientationwillbecalculatedatfirst.The“valleysontheprojectionre

17、sultareregardedasthespacebetweencharactersandusedtosegmentcharactersfromeachother.SegmentedregionsarefurtherprocessedbyverticalprojectiontoobtainpreciseboundingboxesoftheLPcharacters.SincesimplesegmentationmethodsareeasilyaffectedbytherotationofLP,segmentingtheskewedLPbecomesakeyissuetobesolved.Inth

18、ebinarizationalgorithms,globalorlocalmethodsareoftenusedtoobtainforegroundfrombackgroundandthenregionconnectionoperationisusedtoobtaincharacterregions.Inthemostrecentwork,localthresholddeterminationandslidewindowtechniquearedevelopedtoimprovethesegmentationperformance.Intheglobaloptimizationalgorith

19、ms,thegoalisnottoobtaingoodsegmentationresultforindependentcharactersbuttoobtainacompromiseofcharacterspatialarrangementandsinglecharacterrecognitionresult.HiddenMarkovchainhasbeenusedtoformulatethedynamicsegmentationofcharactersinLP.Theadvantageofthealgorithmisthattheglobaloptimizationwillimproveth

20、erobustnesstonoise.Andthedisadvantageisthatpreciseformatdefinitionisnecessarybeforeasegmentationprocess.CharacterandsymbolrecognitionalgorithmsinLPRcanbecategorizedintolearning-basedonesandtemplatematchingones.Fortheformerone,artificialneuralnetwork(ANN)isthemostlyusedmethodsinceitisprovedtobeableto

21、obtainverygoodrecognitionresultgivenalargetrainingset.AnimportantfactorintraininganANNrecognitionmodelforLPistobuildreasonablenetworkstructurewithgoodfeatures.SVM-basedmethodisalsoadoptedinLPRtoobtaingoodrecognitionperformancewithevenfewtrainingsamples.Recently,cascadeclassifiermethodisalsousedforLP

22、recognition.Templatematchingisanotherwidelyusedalgorithm.Generally,researchersneedtobuildtemplateimagesbyhandfortheLPcharactersandsymbols.Theycanassignlargerweightsfortheimportantpoints,forexample,thecornerpoints,inthetemplatetoemphasizethedifferentcharacteristicsofthecharacters.Invarianceoffeaturep

23、ointsisalsoconsideredinthetemplatematchingmethodtoimprovetherobustness.Thedisadvantageisthatitisdifficulttodefinenewtemplatebytheuserswhohavenoprofessionalknowledgeonpatternrecognition,whichwillrestricttheapplicationofthealgorithm.Basedontheabovementionedalgorithms,lotsofLPRmethodshavebeendeveloped.

24、However,thesemethodsaremainlydevelopedforspecificnationorspecialLPformats.InRef.theauthorsfocusonrecognizingGreekLPsbyproposingnewsegmentationandrecognitionalgorithms.ThecharactersonLPsarealphanumericswithseveralfixedformats.InRef.Zhangetal.developedalearning-basedmethodforLPdetectionandcharacterrec

25、ognition.TheirmethodismainlyforLPsofKoreanstyles.InRef.opticalcharacterrecognition(OCR)techniqueareintegratedintoLPRtodevelopgeneralLPRmethod,whiletheperformanceofOCRmaydropwhenfacingLPsofpoorimagequalitysinceitisdifficulttodiscriminaterealcharacterfromcandidateswithoutformatsupervision.Thismethodca

26、nonlyselectcandidatesofbestrecognitionresultsasLPcharacterswithoutrecoveryprocess.Wangetal.developedamethodtorecognizeLPRwithvariousviewingangles.Skewfactorisconsideredintheirmethod.InRef.theauthorsproposedanautomaticLPRmethodwhichcantreatthecasesofchangesof川umination,vehiclespeed,routesandbackgroun

27、ds,whichwasrealizedbydevelopingnewdetectionandsegmentationalgorithmswithrobustnesstothe川uminationandimageblurring.Theperformanceofthemethodisencouragingwhiletheauthorsdonotpresenttherecognitionresultinmultinationormultistyleconditions.InRef.theauthorsproposeanLPRmethodinmultinationalenvironmentwithc

28、haractersegmentationandformatindependentrecognition.Sincenorecognitioninformationisusedincharactersegmentation,falsesegmentedcharactersfrombackgroundnoisemaybeproduced.Whatismore,therecognitionmethodisnotalearning-basedmethod,whichwilllimititsextensibility.InRef.Mposeagenerativerecognitionmethod.Gen

29、erativemodels(GM)areproposedtoproducemanysyntheticcharacterswhosestatisticalvariabilityisequivalent(foreachclass)tothatshowedbyrealsamples.Thusasuitablestatisticaldescriptionofalargesetofcharacterscanbeobtainedbyusingonlyalimitedsetofimages.Asaresult,theextensionabilityofcharacterrecognitionisimprov

30、ed.ThismethodmainlyconcernsthecharacterrecognitionextensibilityinsteadofwholeLPRmethod.FromthereviewwecanseethatLPRmethodinmultistyleLPRwithmultinationalapplicationisnotfullyconsidered.LotsofexistingLPRmethodscanworkverywellinaspecialapplicationconditionwhiletheperformancewilldropsharplywhentheyaree

31、xtendedfromoneconditiontoanother,orfromseveralstylestoothers.多类型车牌识别配置的方法自动车牌识别（LPR）在过去的几十年中的实用技术。许多应用，如自动收费，犯罪的追求和交通执法，已从中受益。虽然一些新技术，如RFID（无线射频识别），WSN（无线传感器网络），等，已提出了汽车身份识别，车牌图像数据仍因其方便、成本低，在目前的智能交通系统不可缺少的技术。车牌识别系统一般分为三个步骤：车牌定位，字符分割和字符识别。检测步骤大致分类LP和非LP区域分割步骤，将符号/字符从彼此在一个LP,只有准确的轮廓，每个字符图像块左为识别和识别步骤，最

32、后将灰度图像块转换成字符/符号通过预定义的识别模型。虽然车牌识别技术有着很长的研究历史，它仍然是推动各种要求而产生的，最常见的一个是LP风格的变化，例如：（1）通过图像采集条件的变化引起的外观变化。风格的变化从一个国家到另一个。风格的变化时，政府发布新的LP格式。我们将其总结为四个因素，即旋转角度，线数，性格类型和格式，在对实际数据的多样式的LP特征综合分析。一般来说，上述四个因素的任何变化都会导致LP的风格或外表的变化进而影响检测，分割和识别算法。如果LP有一个大的旋转角度，水平LP分割和识别算法可能不工作。如果有一个以上的在一个LP的特征线，更多的线分离算法分割处理前需要。与人的性格类型的

33、变化时，我们采用的方法从一个国家到另一个，有能力重新定义识别模型是必要的。更甚的是，LP风格的变化需要调整的方法本身，分割和识别候选字符可以匹配最好用一个LP格式。已经提出了几种方法，近年来跨国LPS或LPS多而很少全面解决上述因素的风格适应问题。他们中的一些人只要求处理跨国LPS的能力通过重新定义的检测和分割规则或识别模型。在本文中，我们提出了一个可配置的车牌识别方法是从一个到另一个适合的风格，特别是从一个国家到另一个，通过定义四个因素作为参数。用户可以约束的参数范围，同时该方法将自我调整，这样可以更快、更准确的识别。类似于现有的车牌识别技术，我们还提供详细的检测，分割和识别算法。不同的是，

34、我们强调了车牌识别和可扩展性的方法而不是multistyleLPS各算法性能的可配置的框架。在过去的几十年中，已经提出了许多方法用于车牌识别包含检测，分割和识别算法。在下面的段落中，这些算法和车牌识别方法的基础上，简要回顾。低压检测算法主要可按特征分为三类，即edgebased算法，基于颜色特征的算法和基于纹理的算法。LP检测最常用的方法是边缘检测和数学形态学的组合。在这些方法中，梯度（边）是第一个从图像中提取和随后的形态空间分析应用于边缘连接到低压区域。另一种方法是计数的边缘在图像行发现密集的边缘地区或描述密集的边缘在LP地区的Hough变换。边缘分析是最简单的方法具有较低的计算复杂度和良好

35、的可扩展性。与edgebased算法相比，基于颜色特征的算法更依赖于应用条件。由于LPS中的国家往往有几个预定义的颜色，研究人员已经定义的颜色模型的分割区域的利益为低压区。这种方法可以通过照明条件影响很大。赢得了较高的召回率和较低的误报率，纹理分类已被用于低压检测。在参考这种方法可以通过照明条件影响很大。赢得了较高的召回率和较低的误报率，纹理分类已被用于低压检测。基姆等人在文献。使用SVM分类器来检测图像块的纹理包含LP像素。在参考文献作者使用Gabor滤波器提取纹理特征的多尺度、multiorientations描述LP区域的纹理特性。在参考文献采用张X和Y的衍生功能，灰度值的方差和AdaB

36、oost分类器，对图像中的LP和非LP区域进行分类，在文献。小波特征分析方法识别低压区。尽管这些方法的计算复杂性限制了他们的可用性，性能良好。此外，基于纹理的算法可以通过多语言因素的影响。多线的LP分割算法可分为三类，即算法的基础上投影，二值化和全局优化。在投影算法，梯度或彩色投影在垂直方向将先计算。“谷”在投影结果作为特征和用于分割字符之间的空间。分割区域的垂直投影的进一步处理以获得精确的包围盒的LP角色。从简单的分割方法很容易通过LP的旋转的影响，对倾余的LP成为亟待解决的关键问题。在二值化算法，全局或局部的方法经常被用来从背景前景并获得区域连接操作是用来获取字符区域。在最近的工作中，局部阈值测定和滑动窗口技术的开发，以提高