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

Our interest in this paper is in meeting a rapidly growing industrial With the rapid advancement of Deep Learning (DL) for computer demand for information extraction from images of documents such vision problems, many DL architectures are available today for as invoices, bills, receipts etc. In practice users are able to provide a document image understanding ([11], [18], [22], [28]). But like most very small number of example images labeled with the information DLbased techniques, training these models from scratch is resource that needs to be extracted. We adopt a novel'two-level''neurodeductive', and data intensive. This is a major stumbling block for industrial approach where (a) we use pre-trained deep neural problems for which collecting and annotating data incur significant networks to populate a relational database with facts about each costs in time and money. In this paper, we use two complementary document-image; and (b) we use a form of deductive reasoning, forms learning to address this problem: related to meta-interpretive learning of transition systems to learn extraction programs: Given task-specific transitions defined using (1) Neural-learning: Using pre-trained DL models for reading the entities and relations identified by the neural detectors and document images and converting them into a structured a small number of instances (usually 1, sometimes 2) of images form by populating a predefined database schema.