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基于CNN網(wǎng)絡的漢字圖像字體識別Title:HandwrittenChineseCharacterFontRecognitionBasedonCNNNetworkAbstract:TherecognitionofhandwrittenChinesecharactersplaysasignificantroleinvariousfieldssuchascharacterrecognition,textdetection,andopticalcharacterrecognition(OCR).OneoftheprimarychallengesinthisareaistoaccuratelyrecognizeChinesecharacterswrittenindifferentfontsorhandwritingstyles.Inthispaper,weproposeaConvolutionalNeuralNetwork(CNN)basedapproachforhandwrittenChinesecharacterfontrecognition.Weconductextensiveexperimentstodemonstratetheeffectivenessofourproposedmethod,achievingpromisingresults.1.IntroductionHandwrittenChinesecharactersarewidelyusedindailylife,fromhandwritingrecognitioninsmartdevicestodocumentanalysisininformationretrieval.However,recognizinghandwrittenChinesecharactersindifferentfontsorstylesremainschallengingduetothelargevariationandcomplexityofcharactershapes.Traditionalmethodsoftenrelyonhand-craftedfeaturesandclassifiers,whichmaynotbeeffectivewhenconfrontedwithdiversehandwritingstyles.Inrecentyears,deeplearningtechniques,especiallyCNNs,haveshownremarkableperformanceinvariousimagerecognitiontasks,motivatingtheirapplicationinhandwrittenChinesecharacterrecognition.2.RelatedWorkPreviousresearchonChinesecharacterrecognitionhasmainlyfocusedonprintedortypedcharacters,withlimitedworkonhandwrittencharacterrecognition.Somestudieshaveexploredfeatureextractionmethods,suchasscale-invariantfeaturetransform(SIFT)andhistogramoforientedgradients(HOG),combinedwithclassifierssuchassupportvectormachines(SVM)andk-nearestneighbors(KNN).Whilethesemethodshaveachievedcertainsuccess,theyheavilyrelyonmanuallydesignedfeaturesandmaynotcapturethecomplexpatternsofhandwrittencharacterseffectively.CNNshavedemonstratedsuperiorperformanceincharacterrecognitiontasks,makingthemapromisingsolutionforhandwrittenChinesecharacterrecognition.3.MethodologyOurproposedapproachconsistsofseveralkeysteps:datasetpreparation,dataaugmentation,CNNarchitecturedesign,andmodeltraining.3.1DatasetPreparationWeconstructalarge-scaledatasetcontainingvarioushandwrittenChinesecharactersfrommultiplefontsandstyles.Eachcharacterismanuallyannotatedwithitscorrespondingfontorstyle,providinggroundtruthlabelsfortrainingandevaluation.3.2DataAugmentationToenhancetherobustnessandgeneralizationabilityofourmodel,weutilizedataaugmentationtechniquessuchasrotation,scaling,andtranslationtoartificiallyexpandthetrainingset.Thishelpsthemodellearninvariantrepresentationsofcharactersregardlessoftheirsizeororientation.3.3CNNArchitectureDesignWedesignaCNNarchitecturespecificallytailoredforhandwrittenChinesecharacterfontrecognition.Thenetworkcomprisesmultipleconvolutionallayers,followedbypoolinglayersandfullyconnectedlayers.Dropoutregularizationisappliedtopreventoverfitting,andrectifiedlinearunit(ReLU)activationfunctionsareusedtointroducenon-linearity.3.4ModelTrainingWerandomlysplitthedatasetintotrainingandvalidationsets.Themodelistrainedusinggradientdescentoptimizationtechniquesandbackpropagationalgorithms.Weemploymini-batchtrainingtoaccelerateconvergenceandadjustthelearningratedynamicallytofine-tunethemodel.4.ExperimentalResultsWeevaluatetheperformanceofourproposedmethodontheconstructeddatasetusingvariousmetricssuchasaccuracy,precision,recall,andF1score.ComparisonswithbaselinemethodsdemonstratetheeffectivenessofourCNN-basedapproach.Wealsoconductexperimentstoevaluatetheimpactofdifferentfactors,suchasthenumberofconvolutionallayers,poolingstrategies,anddropoutrates,ontherecognitionperformance.5.ConclusionInthispaper,weproposeaCNN-basedapproachforhandwrittenChinesecharacterfontrecognition.Ourmethoddemonstratespromisingresultsinhandlingthechallengesposedbydiversefontsandstyles.Theexperimentalevaluationprovestheeffectivenessandrobustnessofourapproach,highlightingthepotentia
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