An Invoice Reading System Using a Graph Convolutional Network

Abstract

In this paper, we present a model-free system for reading digitized invoice images, which highlights the most useful billing entities and does not require any particular parameterization. The power of the system lies in the fact that it generalizes to both seen and unseen layouts of invoice. The system first breaks down the invoice data into various set of entities to extract and then learns structural and semantic information for each entity to extract via a graph structure, which is later generalized to the whole invoice structure. This local neighborhood exploitation is accomplished via a Graph Convolutional Network (GCN). The system digs deep to extract table information and provide complete invoice reading upto 27 entities of interest without any template information or configuration with an excellent overall F-measure score of 0.93.