Novel contour based detection and GrabCut segmentation for sign language recognition
Malladi Sai Phani Kumar, Veerapalli Lathasree, S. N. Karishma · 2017
The main intention of this paper is to build an automatic computer aided hand gesture to voice conversion system for people suffering from Aphonia, a medical term for speech impairment. We have assumed the input gesture images given to the system as simple and complex depending on the background of the image. A novel contour based image segmentation algorithm is proposed in this paper to detect the boundary of the foreground from images with simple dark background. The traditional GrabCut algorithm is employed for segmentation of foreground from images with complex background. This algorithm iteratively segments the image to extract the foreground accurately. The American Sign Language (ASL) 26 finger-spelled alphabet images are taken as the dataset for the two above mentioned algorithms. For the dataset that we have generated, it is observed from the results that contour based segmentation algorithm provides absolutely perfect results. The number of iterations required for GrabCut algorithm to segment the foreground may vary from image to image depending on the background. Out of all the 26 gesture of alphabets, Q, R and S need 6 number of iterations at maximum. A minimum of 1 iteration is required for alphabets E, J and O. On an average, 3 iterations of GrabCut algorithm is required to completely segment the foreground from images with complex background.