  Scientific documents contain tables that list important information in a
concise fashion. Structure and content extraction from tables embedded within
PDF research documents is a very challenging task due to the existence of
visual features like spanning cells and content features like mathematical
symbols and equations. Most existing table structure identification methods
tend to ignore these academic writing features. In this paper, we adapt the
transformer-based language modeling paradigm for scientific table structure and
content extraction. Specifically, the proposed model converts a tabular image
to its corresponding LaTeX source code. Overall, we outperform the current
state-of-the-art baselines and achieve an exact match accuracy of 70.35 and
49.69% on table structure and content extraction, respectively. Further
analysis demonstrates that the proposed models efficiently identify the number
of rows and columns, the alphanumeric characters, the LaTeX tokens, and
symbols.
