Abstract
Automatic hand gesture recognition is the most important part of sign language translation. Its importance increases with the growth of deaf and hard of hearing population and cognitive computing. In this article, we propose an efficient system for automatic hand gesture recognition based on deep learning. The proposed system is based on a convolutional neural network (CNN). It employs a transfer learning of 3D CNN for hand gesture recognition. Three different datasets are used to evaluate the proposed system in signer dependent and signer independent modes.
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