Scholarly papers (journal and conference papers, technical reports, etc.) usually contain multiple "figures" such as plots, flow charts and other images which are generated manually to symbolically represent and illustrate visually important concepts, findings and results. These figures can be analyzed for automated data extraction or semantic analysis. Surprisingly, large scale automated extraction of such figures from PDF documents has received little attention. Here we discuss the challenges of how to build a heuristic independent trainable model for such an extraction task and how to extract figures at scale. Motivated by recent developments in table extraction, we define three new evaluation metrics: figure-precision, figure-recall, and figure-F1-score. Our dataset consists of a sample of 200 PDFs, randomly collected from five million scholarly PDFs and manually tagged for 180 figure locations. Initial results from our work demonstrate an accuracy greater than 80%.