  Most of the knowledge in materials science literature is in the form of
unstructured data such as text and images. Here, we present a framework
employing natural language processing, which automates text and image
comprehension and precision knowledge extraction from inorganic glasses'
literature. The abstracts are automatically categorized using latent Dirichlet
allocation (LDA), providing a way to classify and search semantically linked
publications. Similarly, a comprehensive summary of images and plots are
presented using the 'Caption Cluster Plot' (CCP), which provides direct access
to the images buried in the papers. Finally, we combine the LDA and CCP with
the chemical elements occurring in the manuscript to present an 'Elemental
map', a topical and image-wise distribution of chemical elements in the
literature. Overall, the framework presented here can be a generic and powerful
tool to extract and disseminate material-specific information on
composition-structure-processing-property dataspaces, allowing insights into
fundamental problems relevant to the materials science community and
accelerated materials discovery.
