Templatic documents, such as receipts, bills, insurance quotes, and others, are extremely common and critical in a diverse range of business workflows. Currently, processing these documents is largely a manual effort, and automated systems that do exist are based on brittle and error-prone heuristics. Consider a document type like invoices, which can be laid out in thousands of different ways — invoices from different companies, or even different departments within the same company, may have slightly different formatting. However, there is a common understanding of the structured information that an invoice should contain, such as an invoice number, an invoice date, the amount due, the pay-by date, and the list of items for which the invoice was sent. A system that can automatically extract all this data has the potential to dramatically improve the efficiency of many business workflows by avoiding error-prone, manual work.

In “Representation Learning for Information Extraction from Form-like Documents”, accepted to ACL 2020, we present an approach to automatically extract structured data from templatic documents. In contrast to previous work on extraction from plain-text documents, we propose an approach that uses knowledge of target field types to identify candidate fields. These are then scored using a neural network that learns a dense representation of each candidate using the words in its neighborhood. Experiments on two corpora (invoices and receipts) show that we’re able to generalize well to unseen layouts. Read More