Learning how to extract information from scanned documents

Abstract

In recent years, there has been a lot of interest in methodologies for extracting information from text-based documents. Specifically in the medical field, a recent challenge has been to extract information from different types of scanned medical documents, such as patient registration forms, prescription order forms, and medical history forms. The lack of structure and large variety of information across these documents makes it difficult to automate the process of retrieving data. Today, humans read the documents and manually record the key pieces of information. This thesis focuses on the process of learning how to automate information extraction from a variety of scanned medical documents from a Computer Vision standpoint. We look at two different approaches: an object-detection approach and a text-spotting approach . In each method, we attempt to extract a subset of document fields correctly. We evaluate and compare the results for solving the problem at hand.