Paper Title
Performance Analysis of Supervised Classification Models on Gesture Recognition of Single Handed Alphabet System of Indian Sign Language

This paper studies the performance analysis of different supervised learning classification models in the context of static gesture recognition of the single handed alphabet system, a subset of the Indian Sign Language, with leap motion controller as the apex in recognizing hand gestures. Innovations in the field of machine learning and pattern recognition can be seen prominently in speech recognition, which today can be achieved with high accuracy. But, for the hearing and speech impaired individuals, who are a substantial part of the Indian population (2.78%), an efficient model to recognize the patterns of the articulated hand gestures and convert them to speech, is yet to be found. This paper would especially focus on improving the prediction accuracies of the different supervised classification models in recognition of these hand gestures and would aim to extend the same for dynamic gesture recognition system. Emphasize has been given to take into consideration a diverse dataset that would lead us to stable gesture recognition system with best possible accuracies, the classification models being used are Support Vector Machines, K- Nearest Neighbors and Decision Tree. Finally, we would evaluate the results of the different classifiers, analyze the features and the techniques that were devised to give such results. Keywords - Indian Sign Language (ISL), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision tree (D- Tree), leap Motion controller, machine learning (ML), Stochastic Gradient Descent(SGD), gesture recognition.