A training method for automated sign language recognition, the method comprising: receiving, with an input device, a plurality of training inputs corresponding to a plurality of hand movements and postures, of a user, that correspond to a sequence of signs; the receiving further comprising: receiving, with the input device, a first plurality of inputs corresponding to a plurality of movements of a dominant hand of the user; and receiving, with the input device, a second plurality of inputs corresponding to a plurality of movements of a non-dominant hand of the user, extracting, with the processor, a plurality of features from the training inputs corresponding to the hand movements and postures; the extracting further comprising: extracting a first plurality of features corresponding to finger related features in the dominant hand from the first plurality of inputs; and extracting a second plurality of features corresponding to global angle features in the non-dominant hand from the second plurality of inputs, generating, with the processor, a first Hidden Markov Model to identify a start of the sequence of signs based on a first set of features in the plurality of features from the training inputs corresponding to the hand movements and postures; generating, with the processor, a second Hidden Markov Model to identify additional signs based on a second set of features in the plurality of features from the training inputs corresponding to the hand movements and postures; storing, with the processor, the first Hidden Markov Model, and the second Hidden Markov Model in a memory for recognition of said start of the sequence of signs and said additional signs received from the input device; and generating and then storing in said memory, with the processor, a third Hidden Markov Model based on the first plurality of features from the training inputs and the second plurality of features from the training inputs for identifying transitions between signs in the sequences of signs. receiving, with an input device, a plurality of training inputs corresponding to a plurality of hand movements and postures, of a user, that correspond to a sequence of signs; the receiving further comprising: receiving, with the input device, a first plurality of inputs corresponding to a plurality of movements of a dominant hand of the user; and receiving, with the input device, a second plurality of inputs corresponding to a plurality of movements of a non-dominant hand of the user, extracting, with the processor, a plurality of features from the training inputs corresponding to the hand movements and postures; the extracting further comprising: extracting a first plurality of features corresponding to finger related features in the dominant hand from the first plurality of inputs; and extracting a second plurality of features corresponding to global angle features in the non-dominant hand from the second plurality of inputs, generating, with the processor, a first Hidden Markov Model to identify a start of the sequence of signs based on a first set of features in the plurality of features from the training inputs corresponding to the hand movements and postures; generating, with the processor, a second Hidden Markov Model to identify additional signs based on a second set of features in the plurality of features from the training inputs corresponding to the hand movements and postures; storing, with the processor, the first Hidden Markov Model, and the second Hidden Markov Model in a memory for recognition of said start of the sequence of signs and said additional signs received from the input device; and generating and then storing in said memory, with the processor, a third Hidden Markov Model based on the first plurality of features from the training inputs and the second plurality of features from the training inputs for identifying transitions between signs in the sequences of signs. <EOS>