Automatic dynamic sign language recognition is even more challenging than gesture recognition due to the fact that the vocabularies are large and signs are context dependent. Previous works in this direction tend to build classifiers based on complex hand-crafted features computed from the raw inputs. As a type of deep learning model, convolutional neural networks (CNNs) have significantly advanced the accuracy of human gesture classification. However, such methods are currently used to treat video frames as 2D images and recognize gestures at ...
