SSVM Classifier and Hand Gesture Based Sign Language Recognition
Saket Kumar, Gaurav Kumar Yadav, Hemendra Pal Singh, Sanal Malhotra, Ashutosh Gupta · 2018
Hand gesture recognition is the hot topic in these days in research. This paper is based on hand gesture recognition for sign language. Hand gesture recognition process having eight steps like image acquisition, skin color based segmentation, removal of background, canny edge detection, and PCA Feature extraction, image classification using support vector machine classifier, training of data and testing of data to evaluate appropriate result. The application of this complete system is in several field but we concentrate on two fields such as for security purpose in which different gesture converted in different text code and sentences and second one is for impaired hearing people who can easily communicate with the world in form of text and sentences. In this research work the system is trained and tested for 15 different gestures with one hand and two hand successfully with detection accuracy of 94.5%.