  Information Extraction (IE) for semi-structured document images is often
approached as a sequence tagging problem by classifying each recognized input
token into one of the IOB (Inside, Outside, and Beginning) categories. However,
such problem setup has two inherent limitations that (1) it cannot easily
handle complex spatial relationships and (2) it is not suitable for highly
structured information, which are nevertheless frequently observed in
real-world document images. To tackle these issues, we first formulate the IE
task as spatial dependency parsing problem that focuses on the relationship
among text tokens in the documents. Under this setup, we then propose SPADE
(SPAtial DEpendency parser) that models highly complex spatial relationships
and an arbitrary number of information layers in the documents in an end-to-end
manner. We evaluate it on various kinds of documents such as receipts, name
cards, forms, and invoices, and show that it achieves a similar or better
performance compared to strong baselines including BERT-based IOB taggger.
