A Review on Indian Sign Language Recognition <s> Feature Extraction <s> We propose an algorithm that extracts efficient feature vectors to recognize a hand gesture for sign language. The proposed algorithm recognizes hand gesture based on visual information without using any special gesture glove. To recognize hand gestures, the proposed method is uses three steps. First, by an edge-based hand area search algorithm, a hand block is found and segmented efficiently from the monochrome input images. Second, if the hand area is successfully extracted, the feature vectors representing the hand shape are analyzed applying orientation histogram scheme. Also, the feature vectors of the moving hand is obtained by motion estimation. In the last step, we recognize hand gesture by feature vectors of the hand's shape and movements. The proposed algorithm can not only segment the hand area but also extract the feature vectors from the gray scaled motion images representing 5 sign language words. <s> BIB001 </s> A Review on Indian Sign Language Recognition <s> Feature Extraction <s> This paper presents a novel technique for classifying human hand gestures based on stationary wavelet transform (SWT) and classification based on geometrical based moments and compares the results with the classification based on Hu-moments and wavelet approximate images. The technique uses view-based approach for representation of hand actions, and uses a cumulative image-difference technique where the time between the sequences of images is implicitly captured in the representation of action resulting in Motion History Images (MHI). These MHIs are decomposed into wavelet sub-images using SWT. Translation and scaling invariant moments are then computed from the resulting high scale low pass residue of the wavelet sub-images. These geometrical moments are used as the global image descriptors and are fed to Artificial Neural Network (ANN) for classification. The recognition criterion is established using backpropagation based multilayer perceptron (MLP). The experiments show that such a system can classify human hand gestures with a classification accuracy of 97%. <s> BIB002 </s> A Review on Indian Sign Language Recognition <s> Feature Extraction <s> In this paper we present a novel method for hand gesture recognition based on Gabor filters and support vector machine (SVM). Gabor filters are first convolved with images to acquire desirable hand gesture features. The principal components analysis (PCA) method is then used to reduce the dimensionality of the feature space. With the reduced Gabor features, SVM is trained and exploited to perform the hand gesture recognition tasks. To confirm the robustness of the proposed method, a dataset with large posed-angle (≫45 deg.) of hand gestures is created. The experiment result shows that the recognition rate of 95.2% can be achieved when SVM is used. A real-time video system for hand gesture recognition is also presented with a processing rate of 0.2 s for every frame. This result proves the efficiency and superiority of the proposed Gabor-SVM method. <s> BIB003 </s> A Review on Indian Sign Language Recognition <s> Feature Extraction <s> This paper proposes an automatic gesture recognition approach for Indian Sign Language (ISL). Indian sign language uses both hands to represent each alphabet. We propose an approach which addresses local-global ambiguity identification, inter-class variability enhancement for each hand gesture. Hand region is segmented and detected by YCbCr skin color model reference. The shape, texture and finger features of each hand are extracted using Principle Curvature Based Region (PCBR) detector, Wavelet Packet Decomposition (WPD-2) and complexity defects algorithms respectively for hand posture recognition process. To classify each hand posture, multi class non linear support vector machines (SVM) is used, for which a recognition rate of 91.3% is achieved. Dynamic gestures are classified using Dynamic Time Warping (DTW) with the trajectory feature vector with 86.3% recognition rate. The performance of the proposed approach is analyzed with well known classifiers like SVM, KNN & DTW. Experimental results are compared with the conventional and existing algorithms to prove the better efficiency of the proposed approach. <s> BIB004
Feature extraction is one of the most crucial steps of sign language recognition since the inputs to the classifier are the feature vectors obtained from this step. The techniques used for feature extraction should find shapes reliably and robustly irrespective of changes in illumination levels, position, orientation and size of the object in a video/image. Objects in an image are represented as collection of pixels. For object recognition we need to describe the properties of these groups of pixels. The features can be obtained in different ways: wavelet decomposition BIB002 , Haar wavelets, Haar-like features  , texture features BIB004 , orientation histogram BIB001 , scale invariant feature transform  , Fourier descriptors etc. In some cases the ROI pixels will be used as the feature vector after a dimensionality reduction using Principal Component Analysis (PCA) BIB003 . The feature vector thus obtained using any one of the feature extraction methods is used for training the classifier.