  Theory of convolutional neural networks suggests the property of shift
equivariance, i.e., that a shifted input causes an equally shifted output. In
practice, however, this is not always the case. This poses a great problem for
scene text detection for which a consistent spatial response is crucial,
irrespective of the position of the text in the scene.
  Using a simple synthetic experiment, we demonstrate the inherent shift
variance of a state-of-the-art fully convolutional text detector. Furthermore,
using the same experimental setting, we show how small architectural changes
can lead to an improved shift equivariance and less variation of the detector
output. We validate the synthetic results using a real-world training schedule
on the text detection network. To quantify the amount of shift variability, we
propose a metric based on well-established text detection benchmarks.
  While the proposed architectural changes are not able to fully recover shift
equivariance, adding smoothing filters can substantially improve shift
consistency on common text datasets. Considering the potentially large impact
of small shifts, we propose to extend the commonly used text detection metrics
by the metric described in this work, in order to be able to quantify the
consistency of text detectors.
