  Due to the complexity of modeling the elastic properties of materials, the
use of machine learning algorithms is continuously increasing for tactile
sensing applications. Recent advances in deep neural networks applied to
computer vision make vision-based tactile sensors very appealing for their
high-resolution and low cost. A soft optical tactile sensor that is scalable to
large surfaces with arbitrary shape is discussed in this paper. A supervised
learning algorithm trains a model that is able to reconstruct the normal force
distribution on the sensor's surface, purely from the images recorded by an
internal camera. In order to reduce the training times and the need for large
datasets, a calibration procedure is proposed to transfer the acquired
knowledge across multiple sensors while maintaining satisfactory performance.
