  Key information extraction from document images is of paramount importance in
office automation. Conventional template matching based approaches fail to
generalize well to document images of unseen templates, and are not robust
against text recognition errors. In this paper, we propose an end-to-end
Spatial Dual-Modality Graph Reasoning method (SDMG-R) to extract key
information from unstructured document images. We model document images as
dual-modality graphs, nodes of which encode both the visual and textual
features of detected text regions, and edges of which represent the spatial
relations between neighboring text regions. The key information extraction is
solved by iteratively propagating messages along graph edges and reasoning the
categories of graph nodes. In order to roundly evaluate our proposed method as
well as boost the future research, we release a new dataset named WildReceipt,
which is collected and annotated tailored for the evaluation of key information
extraction from document images of unseen templates in the wild. It contains 25
key information categories, a total of about 69000 text boxes, and is about 2
times larger than the existing public datasets. Extensive experiments validate
that all information including visual features, textual features and spatial
relations can benefit key information extraction. It has been shown that SDMG-R
can effectively extract key information from document images of unseen
templates, and obtain new state-of-the-art results on the recent popular
benchmark SROIE and our WildReceipt. Our code and dataset will be publicly
released.
