  The need of sign language is increasing radically especially to hearing
impaired community. Only few research groups try to automatically recognize
sign language from video, colored gloves and etc. Their approach requires a
valid segmentation of the data that is used for training and of the data that
is used to be recognized. Recognition of a sign language image sequence is
challenging because of the variety of hand shapes and hand motions. Here, this
paper proposes to apply a combination of image segmentation with restoration
using topological derivatives for achieving high recognition accuracy. Image
quality measures are conceded here to differentiate the methods both
subjectively as well as objectively. Experiments show that the additional use
of the restoration before segmenting the postures significantly improves the
correct rate of hand detection, and that the discrete derivatives yields a high
rate of discrimination between different static hand postures as well as
between hand postures and the scene background. Eventually, the research is to
contribute to the implementation of automated sign language recognition system
mainly established for the welfare purpose.
