  We present a comprehensive approach to characterizing labyrinthine structures
that often emerge as a final steady state in pattern forming systems. We employ
machine learning based pattern recognition techniques to identify the types and
locations of topological defects of the local stripe ordering to augment
conventional Fourier analysis. A pair distribution function analysis of the
topological defects reveals subtle differences between labyrinthine structures
which are beyond the conventional characterization methods. We utilize our
approach to highlight a clear morphological transition between two zero-field
labyrinthine structures in single crystal Bi substituted Yttrium Iron Garnet
films. An energy landscape picture is proposed to understand the athermal
dynamics that governs the observed morphological transition. Our work
demonstrates that machine learning based recognition techniques enable novel
studies of rich and complex labyrinthine type structures universal to many
pattern formation systems.
