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脱机手写体汉字智能识别模型与相似样本识别研究.docx

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脱机手写体汉字智能识别模型与相似样本识别研究 Title:OfflineHandwrittenChineseCharacterIntelligentRecognitionModelandSimilarSampleIdentification Abstract: HandwrittenChinesecharacterrecognitionhasbeenachallengingtaskduetothecomplexityandvariabilityofhandwritingstyles.Inthispaper,weproposeanofflinehandwrittenChinesecharacterintelligentrecognitionmodel,combinedwithsimilarsampleidentification.ThismodelutilizesdeeplearningtechniquestoachievehighaccuracyinrecognizinghandwrittenChinesecharacters.Additionally,asimilarsampleidentificationalgorithmisdevelopedtoidentifyandcategorizesimilarcharactersamples,whichcanfurtherenhancetherecognitionaccuracyandimprovetheuserexperienceinvariousapplications. 1.Introduction HandwrittenChinesecharacterrecognitionplaysavitalroleinvariousdomains,suchasdocumentdigitization,personalidentification,andintelligenteducationalsystems.However,theinherentcomplexityandvariationofhandwrittencharactersposesignificantchallengesforaccuraterecognition.Traditionalapproachesoftenrelyonfeatureextractionandhandcraftedrules,whicharehighlydependentonexpertknowledgeandlacktheabilitytohandlevariationseffectively.Toovercometheselimitations,thispaperproposesanofflinehandwrittenChinesecharacterintelligentrecognitionmodel,whichleveragesdeeplearningtechniquesforimprovedaccuracy. 2.RelatedWork PreviousstudiesonChinesecharacterrecognitionmainlyfocusononlinerecognition,whichinvolvescapturingthetemporalinformationofhandwritingstrokes.However,onlinerecognitionrequiresreal-timeinput,makingitlesssuitableforscenarioswhereofflinehandwrittensamplesareavailable.Incontrast,offlinerecognitiondealswithstaticimageswithouttemporalinformation.Deeplearningmodels,suchasconvolutionalneuralnetworks(CNNs)andrecurrentneuralnetworks(RNNs),haveachievedremarkablesuccessinofflinehandwrittencharacterrecognitionforvariouslanguages.Buildingupontheseadvancements,ourproposedmodelaimstoimprovetherecognitionaccuracyspecificallyforofflinehandwrittenChinesecharacters. 3.OfflineHandwrittenChineseCharacterIntelligentRecognitionM