  Automatic sign language recognition (SLR) is an important topic within the
areas of human-computer interaction and machine learning. On the one hand, it
poses a complex challenge that requires the intervention of various knowledge
areas, such as video processing, image processing, intelligent systems and
linguistics. On the other hand, robust recognition of sign language could
assist in the translation process and the integration of hearing-impaired
people, as well as the teaching of sign language for the hearing population.
  SLR systems usually employ Hidden Markov Models, Dynamic Time Warping or
similar models to recognize signs. Such techniques exploit the sequential
ordering of frames to reduce the number of hypothesis. This paper presents a
general probabilistic model for sign classification that combines
sub-classifiers based on different types of features such as position, movement
and handshape. The model employs a bag-of-words approach in all classification
steps, to explore the hypothesis that ordering is not essential for
recognition. The proposed model achieved an accuracy rate of 97% on an
Argentinian Sign Language dataset containing 64 classes of signs and 3200
samples, providing some evidence that indeed recognition without ordering is
possible.
