  Automated arc detection methods are needed to scan the ongoing and
next-generation wide-field imaging surveys, which are expected to contain
thousands of strong lensing systems. Arc finders are also required for a
quantitative comparison between predictions and observations of arc abundance.
Several algorithms have been proposed to this end, but machine learning methods
have remained as a relatively unexplored step in the arc finding process. In
this work we introduce a new arc finder based on pattern recognition, which
uses a set of morphological measurements derived from the Mediatrix
Filamentation Method as entries to an Artificial Neural Network (ANN). We show
a full example of the application of the arc finder, first training and
validating the ANN on simulated arcs and then applying the code on four Hubble
Space Telescope (HST) images of strong lensing systems. The simulated arcs use
simple prescriptions for the lens and the source, while mimicking HST
observational conditions. We also consider a sample of objects from HST images
with no arcs in the training of the ANN classification. We use the training and
validation process to determine a suitable set of ANN configurations, including
the combination of inputs from the Mediatrix method, so as to maximize the
completeness while keeping the false positives low. In the simulations the
method was able to achieve a completeness of about 90% with respect to the arcs
that are input to the ANN after a preselection. However, this completeness
drops to $\sim$ 70% on the HST images. The false detections are of the order of
3% of the objects detected in these images. The combination of Mediatrix
measurements with an ANN is a promising tool for the pattern recognition phase
of arc finding. More realistic simulations and a larger set of real systems are
needed for a better training and assessment of the efficiency of the method.
