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 rst 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
con guration with an excellent overall F-measure score of 0:93.
Devashish Lohani, Belaïd Abdel, Yolande Belaïd. An Invoice Reading System using a Graph Convolutional Network. International Workshop on Robust Reading, Dec 2018, PERTH, Australia. ⟨hal-01960846⟩