Bangla sign language interpretation using bag of features and Support Vector Machine

To complete any process, communication is necessary. Deaf and dumb people use special language to communicate which is known as Sign Language. In this paper, we propose an image processing based model for interpretation of Bangla sign language. In the model, initially YCBCR color components are used to detect the skin color of the user and then extract the Bag of features for each input image. Finally extracted features are feed to the Support Vector Machine (SVM) for training and testing. To validate the proposed model, we use our own dataset where both male and female hand gestures are used. Experimental results show that the proposed model exhibited average 86% accuracy for our tested dataset. In addition, the proposed model outperforms than other state-of-art models by exhibiting higher accuracy.