The Generalized Learning Vector Quantization Model to Recognize Indonesian Sign Language (BISINDO)

There is a fundamental difference between image and gesture recognition where image recognition only works against one frame while gesture recognition works on a sequence of frames. It means that the accuracy formulas implemented on each issue are different. The accuracy of the image recognition is calculated based on the prediction accuracy of each frame, while gesture recognition is based on each sequence of frames. The incompatibility of using these accuracy formulas generate the misleading outputs and interpretation. Thus, the classification model used also needs to be adjusted with this problem. In this paper, we use GLVQ model as a classification algorithm based on machine learning approach to recognize the gestures of Indonesian sign language (BISINDO). However, this algorithm is used to classify every single frame so it needs to be modified by adding a new function for a sequence of frames, e.g. mode. In addition, there is a parameter known as the number of prototypes that affects the accuracy of the model. Based on the results of this research, GLVQ model with mode function has a higher degree of accuracy when compared with Hidden-Markov Model (HMM) in recognizing BISINDO. However, it is necessary to specify a more appropriate function instead of mode which is not give uniquely results. We also know that the increasing number of prototypes does not increase the accuracy significantly. In fact, the increasing number of prototypes used can increase the computational time.