  Different types of spectroscopies, such as X-ray absorption near edge
structure (XANES) and Raman spectroscopy, play a very important role in
analyzing the characteristics of different materials. In scientific literature,
XANES/Raman data are usually plotted in line graphs which is a visually
appropriate way to represent the information when the end-user is a human
reader. However, such graphs are not conducive to direct programmatic analysis
due to the lack of automatic tools. In this paper, we develop a plot digitizer,
named Plot2Spectra, to extract data points from spectroscopy graph images in an
automatic fashion, which makes it possible for large scale data acquisition and
analysis. Specifically, the plot digitizer is a two-stage framework. In the
first axis alignment stage, we adopt an anchor-free detector to detect the plot
region and then refine the detected bounding boxes with an edge-based
constraint to locate the position of two axes. We also apply scene text
detector to extract and interpret all tick information below the x-axis. In the
second plot data extraction stage, we first employ semantic segmentation to
separate pixels belonging to plot lines from the background, and from there,
incorporate optical flow constraints to the plot line pixels to assign them to
the appropriate line (data instance) they encode. Extensive experiments are
conducted to validate the effectiveness of the proposed plot digitizer, which
shows that such a tool could help accelerate the discovery and machine learning
of materials properties.
