  Document image classification remains a popular research area because it can
be commercialized in many enterprise applications across different industries.
Recent advancements in large pre-trained computer vision and language models
and graph neural networks has lent document image classification many tools.
However using large pre-trained models usually requires substantial computing
resources which could defeat the cost-saving advantages of automatic document
image classification. In the paper we propose an efficient document image
classification framework that uses graph convolution neural networks and
incorporates textual, visual and layout information of the document. We have
rigorously benchmarked our proposed algorithm against several state-of-art
vision and language models on both publicly available dataset and a real-life
insurance document classification dataset. Empirical results on both publicly
available and real-world data show that our methods achieve near SOTA
performance yet require much less computing resources and time for model
training and inference. This results in solutions than offer better cost
advantages, especially in scalable deployment for enterprise applications. The
results showed that our algorithm can achieve classification performance quite
close to SOTA. We also provide comprehensive comparisons of computing
resources, model sizes, train and inference time between our proposed methods
and baselines. In addition we delineate the cost per image using our method and
other baselines.
