Issue 5, 2022

Plot2Spectra: an automatic spectra extraction tool

Abstract

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, the 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, the 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 could help accelerate the discovery and machine learning of materials properties.

Graphical abstract: Plot2Spectra: an automatic spectra extraction tool

Article information

Article type
Paper
Submitted
12 nov. 2021
Accepted
05 août 2022
First published
23 août 2022
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2022,1, 719-731

Plot2Spectra: an automatic spectra extraction tool

W. Jiang, K. Li, T. Spreadbury, E. Schwenker, O. Cossairt and M. K. Y. Chan, Digital Discovery, 2022, 1, 719 DOI: 10.1039/D1DD00036E

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