![]() The first stage involves an axis alignment module. 1, there are two stages in the plot digitizer. In this paper, we develop Plot2Spectra, which transforms plot lines from graph images into sets of coordinates in an automatic fashion. However, the burden of having to manually align axes, input tick values, pick out the color of the target plot, and draw the region where the plot falls in is cumbersome and not conducive to automation. WebPlotDigitizer 5 is one of the most popular plot data extraction tools to date. However, the approach and tool can be applied to other types of graph images.Įarlier work 2–4 on extracting plot lines from the graph images focus on dealing with plot lines with pivot points, which are likely to fail if the assumption does not hold. We use as prototypical examples XANES and Raman spectroscopy graphs, which often have a series of difficult-to-separate line plots within the same image. It is therefore highly desirable to develop a tool for the digitization of spectroscopy graphical plots. In particular, high-quality experimental spectroscopy data is critical for the development of machine learning (ML) models, and the difficulty involved in extracting such data from the scientific literature hinders efforts in ML of materials properties. As a result, other researchers have to use interactive plot data extraction tools to extract data points from the graph image, which makes it difficult for large scale data acquisition and analysis. However, it is not standard for materials researchers to release raw data along with their publications. For the purpose of understanding the insights behind these measurements, data points are usually displayed in graphical form within scientific journal articles. In materials science, in particular, X-ray absoprtion near edge structure (XANES) and Raman spectroscopies play a very important role in analyzing the characteristics of materials at the atomic level. 1 Introduction Spectroscopy, primarily in the electromagnetic spectrum, is a fundamental exploratory tool in the fields of physics, chemistry, and astronomy, allowing the composition, physical structure, and electronic structure of matter to be investigated at the atomic, molecular, and macro scale, and over astronomical distances. 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. 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. We also apply scene text detector to extract and interpret all tick information below the x-axis. 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. Specifically, the plot digitizer is a two-stage framework. 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. ![]() However, such graphs are not conducive to direct programmatic analysis due to the lack of automatic tools. 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. 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.
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