A Novel Feature Extraction for American Sign Language Recognition Using Webcam - Thongtawee, Ariya; Pinsanoh, Onamon; Kitjaidure, Yuttana (My University, 2019)
Sign language is physical communication for contributing the meaning instead of using voice to demonstrate communicator's opinion. This paper introduces a simple and efficient algorithm for feature extraction to recognize American Sign Language alphabets from both static and dynamic gestures. The proposed algorithm comprises of four different techniques: Number of white pixels at the edge of the image (NwE), Finger length from the centroid point (Fcen), Angles between fingers (AngF) and Differences of angles between fingers of the first and last frame (delAng). After extracting features from video images, an Artificial Neural Network (ANN) is used to classify the signs. The result of these experiments is achieved up to 95% recognition rate, which is clearly to be the highest accuracy comparing with the other research worked in this field.
Keywords: American Sign Language (ASL); Artificial Neural Network; Feature Extraction; Hand gesture recognition; Webcam