  Due to recent improvements in image resolution and acquisition speed,
materials microscopy is experiencing an explosion of published imaging data.
The standard publication format, while sufficient for traditional data
ingestion scenarios where a select number of images can be critically examined
and curated manually, is not conducive to large-scale data aggregation or
analysis, hindering data sharing and reuse. Most images in publications are
presented as components of a larger figure with their explicit context buried
in the main body or caption text, so even if aggregated, collections of images
with weak or no digitized contextual labels have limited value. To solve the
problem of curating labeled microscopy data from literature, this work
introduces the EXSCLAIM! Python toolkit for the automatic EXtraction,
Separation, and Caption-based natural Language Annotation of IMages from
scientific literature. We highlight the methodology behind the construction of
EXSCLAIM! and demonstrate its ability to extract and label open-source
scientific images at high volume.
