  Recent works on form understanding mostly employ multimodal transformers or
large-scale pre-trained language models. These models need ample data for
pre-training. In contrast, humans can usually identify key-value pairings from
a form only by looking at layouts, even if they don't comprehend the language
used. No prior research has been conducted to investigate how helpful layout
information alone is for form understanding. Hence, we propose a unique
entity-relation graph parsing method for scanned forms called LAGNN, a
language-independent Graph Neural Network model. Our model parses a form into a
word-relation graph in order to identify entities and relations jointly and
reduce the time complexity of inference. This graph is then transformed by
deterministic rules into a fully connected entity-relation graph. Our model
simply takes into account relative spacing between bounding boxes from layout
information to facilitate easy transfer across languages. To further improve
the performance of LAGNN, and achieve isomorphism between entity-relation
graphs and word-relation graphs, we use integer linear programming (ILP) based
inference. Code is publicly available at https://github.com/Bhanu068/LAGNN
