  The robustness of a model for real-world deployment is decided by how well it
performs on unseen data and distinguishes between in-domain and out-of-domain
samples. Visual document classifiers have shown impressive performance on
in-distribution test sets. However, they tend to have a hard time correctly
classifying and differentiating out-of-distribution examples. Image-based
classifiers lack the text component, whereas multi-modality transformer-based
models face the token serialization problem in visual documents due to their
diverse layouts. They also require a lot of computing power during inference,
making them impractical for many real-world applications. We propose, GVdoc, a
graph-based document classification model that addresses both of these
challenges. Our approach generates a document graph based on its layout, and
then trains a graph neural network to learn node and graph embeddings. Through
experiments, we show that our model, even with fewer parameters, outperforms
state-of-the-art models on out-of-distribution data while retaining comparable
performance on the in-distribution test set.
