Gesture interaction is today recognized as a natural, intuitive way to execute commands of an interactive system. For this purpose, several stroke gesture recognizers become more efficient in recognizing end-user gestures from a training set. Although the rate algorithms propose their rates of return there is a deficiency in knowing which is the most recommended algorithm for its use. In the same way, the experiments known by the most successful algorithms have been carried out under different conditions, resulting in non-comparable results. To better understand their respective algorithmic efficiency, this paper compares the recognition rate, the error rate, and the recognition time of five reference stroke gesture recognition algorithms, i.e., 1,P, Q,!FTL,andPennyPincher,onthreediversegesturesets,i.e.,NicIcon,HHReco,andUtopianoAlphabet,inauser−independentscenario.Similarconditionswereappliedtoallalgorithms,tobeexecutedunderthesamecharacteristics.Forthealgorithmsstudied,themethodagreedtoevaluatetheerrorrateandperformancerate,aswellastheexecutiontimeofeachofthesealgorithms.AsoftwaretestingenvironmentwasdevelopedinJavaScripttoperformthecomparativeanalysis.Theresultsofthisanalysishelprecommendingarecognizerwhereitturnsouttobethemostefficient.!FTL(NLSD)isthebestrecognitionrateandthemostefficientalgorithmfortheHHrecoandNicIcondatasets.However,PennyPincherwasthefasteralgorithmforHHrecodatasets.Finally,1 obtained the best recognition rate for the Utopiano Alphabet dataset