  The automatic interpretation of sign languages is a challenging task, as it
requires the usage of high-level vision and high-level motion processing
systems for providing accurate image perception. In this paper, we use
Convolutional Neural Networks (CNNs) and transfer learning in order to make
computers able to interpret signs of the Swedish Sign Language (SSL) hand
alphabet. Our model consist of the implementation of a pre-trained InceptionV3
network, and the usage of the mini-batch gradient descent optimization
algorithm. We rely on transfer learning during the pre-training of the model
and its data. The final accuracy of the model, based on 8 study subjects and
9,400 images, is 85%. Our results indicate that the usage of CNNs is a
promising approach to interpret sign languages, and transfer learning can be
used to achieve high testing accuracy despite using a small training dataset.
Furthermore, we describe the implementation details of our model to interpret
signs as a user-friendly web application.
