  The Human-Machine Interaction (HMI) research field is an important topic in
machine learning that has been deeply investigated thanks to the rise of
computing power in the last years. The first time, it is possible to use
machine learning to classify images and/or videos instead of the traditional
computer vision algorithms. The aim of this paper is to build a symbiosis
between a convolutional neural network (CNN) and a recurrent neural network
(RNN) to recognize cultural/anthropological Italian sign language gestures from
videos. The CNN extracts important features that later are used by the RNN.
With RNNs we are able to store temporal information inside the model to provide
contextual information from previous frames to enhance the prediction accuracy.
Our novel approach uses different data augmentation techniques and
regularization methods from only RGB frames to avoid overfitting and provide a
small generalization error.
