Curve separation for line graphs in scholarly documents

Abstract

Line graphs are abundant in scholarly papers. They are usually generated from a data table and that data can not be accessed. One important step in an automated data extraction pipeline is the curve separation problem: segmenting the pixels into separate curves. Previous work in this domain has focused on raster graphics extracted from scholarly PDFs, whereas most scholarly plots are embedded as vector graphics. We report a system to extract these plots as SVG images and show how that can improve both the accuracy (90%) and the scalability (5-8 seconds) of the curve separation problem.