Euclidean Distance Based Classifier for Recognition and Generating Kannada Text Description from Live Sign Language Video

Abstract

Sign language recognition has emerged in concert of the vital space of analysis in computer Vision. The problem long-faced by the researchers is that the instances of signs vary with each motion and look. Thus, during this paper a completely unique approach for recognizing varied alphabets of Kannada linguistic communication is projected wherever continuous video sequences of the signs are thought of. The system includes of three stages: Preprocessing stage, Feature Extraction and Classification. Preprocessing stage includes skin filtering, bar histogram matching. Eigen values and Eigen Vectors were thought of for feature extraction stage and at last Eigen value weighted Euclidean distance is employed to acknowledge the sign. It deals with vacant hands, so permitting the user to act with the system in natural manner. We have got thought of completely different alphabets within the video sequences and earned a hit rate of 95.25%.