  Sign Language Recognition (SLR) is an important step in facilitating the
communication among deaf people and the rest of society. Existing Persian sign
language recognition systems are mainly restricted to static signs which are
not very useful in everyday communications. In this study, a dynamic Persian
sign language recognition system is presented. A collection of 1200 videos were
captured from 12 individuals performing 20 dynamic signs with a simple white
glove. The trajectory of the hands, along with hand shape information were
extracted from each video using a simple region-growing technique. These
time-varying trajectories were then modeled using Hidden Markov Model (HMM)
with Gaussian probability density functions as observations. The performance of
the system was evaluated in different experimental strategies.
Signer-independent and signer-dependent experiments were performed on the
proposed system and the average accuracy of 97.48% was obtained. The
experimental results demonstrated that the performance of the system is
independent of the subject and it can also perform excellently even with a
limited number of training data.
