Gradient Feature based Static Sign Language Recognition - Prasad, M. Mahadeva (International Journal of Computer Sciences and Engineering, 2018)
In this paper, the work carried out to design the gradient feature based static sign language is presented. Sign languages are the gestures used by the hearing and speaking impaired people for communication. The sign languages are classified into static or dynamic or both static and dynamic sign languages. In static sign languages, still hand postures are used to convey information. In the dynamic sign languages, sequence of hand postures is used to convey information. In the present work, efforts have been made to design the computer vision based static sign language recognition system for the American Sign Language alphabets. The images that represent the static sign language alphabets are grouped into training and test images. The training sign language images are subjected to preprocessing. From the preprocessed images, magnitude and direction gradient features are extracted. These features are used to train the recognition system. The test images are subjected to preprocessing and feature extraction. The extracted features from the test sign language images are used to test the designed sign language recognition system. To classify the static sign language hand gestures, nearest neighbor classifier has been used. Independent experiments are carried out to evaluate the performance of the gradient magnitude and the gradient direction features. The average recognition accuracy of 95.4% for magnitude gradient feature and 80.3% for direction gradient feature are obtained.