  We investigate the usage of a Schlieren imaging setup to measure the
geometrical dimensions of a plasma channel in atomic vapor. Near resonant probe
light is used to image the plasma channel in a tenuous vapor and machine
learning techniques are tested for extracting quantitative information from the
images. By building a database of simulated signals with a range of plasma
parameters for training Deep Neural Networks, we demonstrate that they can
extract from the Schlieren images reliably and with high accuracy the location,
the radius and the maximum ionization fraction of the plasma channel as well as
the width of the transition region between the core of the plasma channel and
the unionized vapor. We test several different neural network architectures
with supervised learning and show that the parameter estimations supplied by
the networks are resilient with respect to slight changes of the experimental
parameters that may occur in the course of a measurement.
