  This paper presents GeoContrastNet, a language-agnostic framework to
structured document understanding (DU) by integrating a contrastive learning
objective with graph attention networks (GATs), emphasizing the significant
role of geometric features. We propose a novel methodology that combines
geometric edge features with visual features within an overall two-staged
GAT-based framework, demonstrating promising results in both link prediction
and semantic entity recognition performance. Our findings reveal that combining
both geometric and visual features could match the capabilities of large DU
models that rely heavily on Optical Character Recognition (OCR) features in
terms of performance accuracy and efficiency. This approach underscores the
critical importance of relational layout information between the named text
entities in a semi-structured layout of a page. Specifically, our results
highlight the model's proficiency in identifying key-value relationships within
the FUNSD dataset for forms and also discovering the spatial relationships in
table-structured layouts for RVLCDIP business invoices. Our code and pretrained
models will be accessible on our official GitHub.
