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Deaf and hard of hearing people communicate with others and within their own communities by using sign language. Computer recognition of sign language begins with the learning of sign gestures and continues through the production of text and speech. There are two types of sign gestures: static and dynamic. Both gesture recognition systems are crucial to the human community, even if static gesture recognition is easier than dynamic gesture recognition. We have conducted research on the steps required to convert static American sign language (ASL) to readable text and selected the best available methods to do so. Examined general steps are the data collection, pre-processing, transformation, feature extraction, and classification. There are also some recommendations for further study in this field.

This paper presents characterization of unconstraint depth photopolymerization yielding different forms of cured voxel under exposure of hollow Gaussian beam. The governing model consists of nonlinear Schrodinger equation along with transient diffusion phenomenon and intensity dependent refractive gradient is considered in predicting the curing behaviour. The effect of various process parameters viz; intensity of and degree of hollowness of Gaussian beam, time of exposure on the formation of cured voxel is presented in this paper. Typical of cured voxel resembles hollow microneedle under certain conditions of exposure. The study proposes potential possibility to be used as one of the methods to develop hollow microneedle being used in many biomedical applications for effective drug delivery.