4-Camera model for sign language recognition using elliptical fourier descriptors and ANN

Abstract

Sign language recognition (SLR) is considered a multidisciplinary research area engulfing image processing, pattern recognition and artificial intelligence. The major hurdle for a SLR is the occlusions of one hand on another. This results in poor segmentations and hence the feature vector generated result in erroneous classifications of signs resulting in deprived recognition rate. To overcome this difficulty we propose in this paper a 4 camera model for recognizing gestures of Indian sign language. Segmentation for hand extraction, shape feature extraction with elliptical Fourier descriptors and pattern classification using artificial neural networks with backpropagation training algorithm. The classification rate is computed and which provides experimental evidence that 4 camera model outperforms single camera model.