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The need of sign language is increasing radically especially to hearingimpaired community. Only few research groups try to automatically recognizesign language from video, colored gloves and etc. Their approach requires avalid segmentation of the data that is used for training and of the data thatis used to be recognized. Recognition of a sign language image sequence ischallenging because of the variety of hand shapes and hand motions. Here, thispaper proposes to apply a combination of image segmentation with restorationusing topological derivatives for achieving high recognition accuracy. Imagequality measures are conceded here to differentiate the methods bothsubjectively as well as objectively. Experiments show that the additional useof the restoration before segmenting the postures significantly improves thecorrect rate of hand detection, and that the discrete derivatives yields a highrate of discrimination between different static hand postures as well asbetween hand postures and the scene background. Eventually, the research is tocontribute to the implementation of automated sign language recognition systemmainly established for the welfare purpose.

A Topological derivative based image segmentation for sign language recognition system using isotropic filter