  As the Portable Document Format (PDF) file format increases in popularity,
research in analysing its structure for text extraction and analysis is
necessary. Detecting headings can be a crucial component of classifying and
extracting meaningful data. This research involves training a supervised
learning model to detect headings with features carefully selected through
recursive feature elimination. The best performing classifier had an accuracy
of 96.95%, sensitivity of 0.986 and a specificity of 0.953. This research into
heading detection contributes to the field of PDF based text extraction and can
be applied to the automation of large scale PDF text analysis in a variety of
professional and policy based contexts.
