Learning how to extract information from scanned documents

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Consul, Natasha

Advisor dc:contributor.advisor

• Regina Barzilay.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119515

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119515