MatGD: Materials Graph Digitizer
Jaewoong Lee Wonseok Lee Jihan Kim
We have developed MatGD (Material Graph Digitizer), which is a tool for
digitizing a data line from scientific graphs. The algorithm behind the tool
consists of four steps: (1) identifying graphs within subfigures, (2)
separating axes and data sections, (3) discerning the data lines by eliminating
irrelevant graph objects and matching with the legend, and (4) data extraction
and saving. From the 62,534 papers in the areas of batteries, catalysis, and
MOFs, 501,045 figures were mined. Remarkably, our tool showcased performance
with over 99% accuracy in legend marker and text detection. Moreover, its
capability for data line separation stood at 66%, which is much higher compared
to other existing figure mining tools. We believe that this tool will be
integral to collecting both past and future data from publications, and these
data can be used to train various machine learning models that can enhance
material predictions and new materials discovery.