2026-09-19T02:25:15Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1195152026-06-06T00:49:26Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Regina Barzilay.
Consul, Natasha
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-12-11T20:38:21Z
2018-12-11T20:38:21Z
2017
http://hdl.handle.net/1721.1/119515
1066344941
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis. "September 2017."
Includes bibliographical references (pages 58-62).
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.
by Natasha Consul.
M.Eng.
62 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Learning how to extract information from scanned documents
Thesis
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