Parsing Line Chart Images Using Linear Programming

Author: Hajime Kato, Mitsuru Nakazawa, Hsuan-Kung Yang, Mark Chen, Bjorn Stenger


This paper proposes a method for automatically recovering data from chart images. In particular we focus on the task
of estimating line charts, as the most common chart type, in a fully automatic way that handles line occlusions, as well as lines of different styles, e.g. dashed or dotted. For this, we first train a single semantic segmentation network
to predict probability maps for each different line styles. We then construct a graph based on this output and formulate the line tracing task as a minimum-cost-flow problem, optimizing a cost function using linear programming. From the traced lines, the axes, and text labels, we recover the numerical values used to generate the chart. In experiments on six datasets, containing both synthesized and crawled images, we show significant improvements over prior work.

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