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

One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis