Our interest in this paper is in meeting a rapidly growing industrial demand
for information extraction from images of documents such as invoices, bills,
receipts etc. In practice users are able to provide a very small number of
example images labeled with the information that needs to be extracted. We
adopt a novel two-level neuro-deductive, approach where (a) we use pre-trained
deep neural networks to populate a relational database with facts about each
document-image; and (b) we use a form of deductive reasoning, related to
meta-interpretive learning of transition systems to learn extraction programs:
Given task-specific transitions defined using the entities and relations
identified by the neural detectors and a small number of instances (usually 1,
sometimes 2) of images and the desired outputs, a resource-bounded
meta-interpreter constructs proofs for the instance(s) via logical deduction; a
set of logic programs that extract each desired entity is easily synthesized
from such proofs. In most cases a single training example together with a
noisy-clone of itself suffices to learn a program-set that generalizes well on
test documents, at which time the value of each entity is determined by a
majority vote across its program-set. We demonstrate our two-level
neuro-deductive approach on publicly available datasets ("Patent" and "Doctor's
Bills") and also describe its use in a real-life industrial problem.

采用两级神经推理方法和预训练深度神经网络，通过逻辑推理来学习实体和关系，生成抽取方案，从而解决发票、帐单、收据等文档图像信息提取的问题。