Sign Language Hand Gesture Recognition Research Articles

Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays

Signed languages are not as pervasive a conversational medium as spoken languages due to the history of institutional suppression of the former and the linguistic hegemony of the latter. This has led to a communication barrier between signers and non-signers that could be mitigated by technology-mediated approaches. Here, we show that a wearable sign-to-speech translation system, assisted by machine learning, can accurately translate the hand gestures of American Sign Language into speech. The wearable sign-to-speech translation system is composed of yarn-based stretchable sensor arrays and a wireless printed circuit board, and offers a high sensitivity and fast response time, allowing real-time translation of signs into spoken words to be performed. By analysing 660 acquired sign language hand gesture recognition patterns, we demonstrate a recognition rate of up to 98.63% and a recognition time of less than 1 s. Wearable yarn-based stretchable sensor arrays, combined with machine learning, can be used to translate American Sign Language into speech in real time.

Spelled sign word recognition using key frame

In this study, the authors present a new system for sign language hand gesture recognition. Using video input, the system can recognise any spelled word or alphabetic sequence signed in American Sign Language. The three main steps in the recognition process include detection of the region of interest (the hands), detection of key frames and recognition of gestures from these key frames. The proposed segmentation algorithm distinguishes regions of interest from both uniform and non-uniform backgrounds with an efficiency of 95%. The proposed key frame detection algorithm achieves an efficiency of 96.50%. A rotation-invariant algorithm for feature extraction is additionally proposed, which provides an overall gesture recognition efficiency of 84.2%.