  Rectifying the orientation of images represents a daily task for every
photographer. This task may be complicated even for the human eye, especially
when the horizon or other horizontal and vertical lines in the image are
missing. In this paper we address this problem and propose a new deep learning
network specially adapted for image rotation correction: we introduce the
rectangle-shaped depthwise convolutions which are specialized in detecting long
lines from the image and a new adapted loss function that addresses the problem
of orientation errors.
  Compared to other methods that are able to detect rotation errors only on few
image categories, like man-made structures, the proposed method can be used on
a larger variety of photographs e.g., portraits, landscapes, sport, night
photos etc. Moreover, the model is adapted to mobile devices and can be run in
real time, both for pictures and for videos. An extensive evaluation of our
model on different datasets shows that it remarkably generalizes, not being
dependent on any particular type of image. Finally, we significantly outperform
the state-of-the-art methods, providing superior results.
