Conversion of Sign Language to Text and Speech and Prediction of Gesture

Abstract

Deaf people rely on sign language to express their own thoughts and feelings. It becomes the major communication barrier between the deaf and other people. Sign Language has evolved as one of the major areas of research and study in computer vision. Researchers in sign language recognition used different input devices such as data gloves, web camera, depth camera, color camera, Microsoft's Kinect sensor, etc. to capture hand signs. In this paper, we display the importance of Sign Language and proposed technique for classification and their efficient results. A sign language looks up the manual communication and body language to convey meaning, as opposed to acoustically conveyed sound patterns, which involve a simultaneous combination of hand shapes, orientation, and movement of hands. The signs are captured using a new digital sensor called “Leap Motion Controller”. LMC is 3D non-contact motion sensor which can track and detects hands, fingers, bones and finger-like objects. The Leap device tracks the data like point, wave, reach, grab which is generated by a leap motion controller. The system implements Dynamic Time Warping (DTW) for converting the hand gestures into an appropriate text.