Sign language is the basic medium of communication for the deaf and dumb people. It has evolved as one of the major areas of research and study in Computer Vision. In this paper we display the importance of Indian Sign Language and proposed techniques for feature extraction and their efficient results. Indian Sign Language has a total of 26 alphabets using either one hand or both hands to show the sign. With the help of energy compaction using discrete cosine transform, maximum energy is packed into lows frequency region. In order to ensure efficient feature extraction and enabling feature vector size to be as small as possible, this paper proposes a novel technique to perform feature extraction and obtain high efficiency. Two techniques have been proposed with regard to reduced complexity and give better efficiency out of which the second approach of considering a feature vector of size 3 has been proved to be the best. It results in least computational complexity in query optimization and further gives 84.61% accuracy in detection of signs. This paper presents the comparison among various transforms for feature extraction from hand sign images. The proposed techniques for feature extraction are executed on a dataset of 260 images (consisting of 10 images of each alphabet).

Sign language is the basic medium of communication for the deaf and dumb people. It is a language which uses manual communication and body language to convey meaning. This can involve combining hand shapes, orientation and movement of hands. Communication may be the biggest challenge for the deaf and dumb in order to receive and convey information, ideas and feelings. Thus, in order to bridge the gap between them and the others, it becomes necessary to build a communicator and translator to translate American Sign Language to Indian and vice versa. In addition to this, the American and Indian sign language is also converted to text and back. During this translation, in order to ensure efficient skin detection and further processing of image, the paper focuses on obtaining appropriate results on Indian sign images based on background removal algorithms. This paper presents the comparison among various color spaces for skin detection based on background removal from hand sign images. The color space is a useful way to specify and conceptualize the color capabilities of a particular digital file or an image. The proposed techniques on colour spaces are executed on a dataset of 78 images. In order to analyze the results of the image based on various color spaces, this study of comparison among them is needed. It elaborates mainly on four color models : RGB, YCbCr, HSV, NTSC. This paper analyses the results of the above color spaces.