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基于支持向量机的英文字符识别研究.docx

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基于支持向量机的英文字符识别研究 Introduction: SupportVectorMachine(SVM)isapopularmachinelearningtechniquethathasbeenusedinthefieldofpatternrecognition.Itisasupervisedlearningalgorithmthatcanclassifydatabyfindingthebesthyperplaneinthefeaturespacethatseparatesdifferentclassesofdatapoints.Englishcharacterrecognitionisoneofthemostfundamentalapplicationsofpatternrecognition,andSVMhasbeenproventobeeffectiveinthisarea.Inthispaper,wewillexploretheresearchonSVM-basedEnglishcharacterrecognitionanddiscussitssignificanceandlimitations. Background: SVM-basedcharacterrecognitionhasalonghistoryandhasbeenwidelyusedforhandwritingrecognition,opticalcharacterrecognition,andotherapplications.Theinputtothealgorithmisasetoffeaturesextractedfromtheimageofthecharacter,andtheoutputistheclassificationofthecharacter.Thefeatureextractionprocessiscritical,asitdeterminestheperformanceofthealgorithm.Variousfeatureextractiontechniqueshavebeenproposed,includingFourierdescriptors,Zernikemoments,andGaborfilters. Methodology: SeveralstudieshavebeenconductedonSVM-basedEnglishcharacterrecognitionusingdifferentfeatureextractiontechniques.Forexample,astudyconductedbyJiangetal.[1]usedZernikemomentsforfeatureextractionandachievedanaccuracyof97.74%.AnotherstudybyLimetal.[2]usedGaborfiltersandachievedanaccuracyof98.3%.Inaddition,Dohareetal.[3]usedacombinationofFourierdescriptorsandshapefeaturesandachievedanaccuracyof99%. Discussion: SVM-basedEnglishcharacterrecognitionhasseveraladvantages.SVMiscapableofhandlinghigh-dimensionaldata,whichisimportantforfeatureextraction.SVMisalsoarobustalgorithmthatcanhandledatawithoutliersandnoise.Inaddition,SVMhasgoodgeneralizationability,whichmeansthatitcanperformwellonnew,unseendata.Despitetheseadvantages,SVMhassomelimitations.Theperformanceofthealgorithmishighlydependentonthequalityofthefeatureextractionprocess,whichcanbeachallengingtask.Moreover,theSVMalgorithmiscomputationallyexpensive,whichcanbeaproblemwhendealingwithlargedatasets. Conclusion: SVM-basedEnglishcharacterrecognitionisapow