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We present an extensible method for identifying semantic points to reverseengineer (i.e. extract the values of) data charts, particularly those inscientific articles. Our method uses a point proposal network (akin to regionproposal networks for object detection) to directly predict the position ofpoints of interest in a chart, and it is readily extensible to multiple charttypes and chart elements. We focus on complex bar charts in the scientificliterature, on which our model is able to detect salient points with anaccuracy of 0.8705 F1 (@1.5-cell max deviation); it achieves 0.9810 F1 onsynthetically-generated charts similar to those used in prior works. We alsoexplore training exclusively on synthetic data with novel augmentations,reaching surprisingly competent performance in this way (0.6621 F1) on realcharts with widely varying appearance, and we further demonstrate our unchangedmethod applied directly to synthetic pie charts (0.8343 F1). Datasets, trainedmodels, and evaluation code are available athttps://github.com/BNLNLP/PPN_model.

An extensible point-based method for data chart value detection