Learning how to extract information from scanned documents
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-s...
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ndltd-MIT-oai-dspace.mit.edu-1721.1-1195152019-05-02T15:51:17Z Learning how to extract information from scanned documents Consul, Natasha Regina Barzilay. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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. 2018-12-11T20:38:21Z 2018-12-11T20:38:21Z 2017 Thesis http://hdl.handle.net/1721.1/119515 1066344941 eng 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 62 pages application/pdf Massachusetts Institute of Technology |
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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. |
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Regina Barzilay. |
author_facet |
Regina Barzilay. Consul, Natasha |
author |
Consul, Natasha |
author_sort |
Consul, Natasha |
title |
Learning how to extract information from scanned documents |
title_short |
Learning how to extract information from scanned documents |
title_full |
Learning how to extract information from scanned documents |
title_fullStr |
Learning how to extract information from scanned documents |
title_full_unstemmed |
Learning how to extract information from scanned documents |
title_sort |
learning how to extract information from scanned documents |
publisher |
Massachusetts Institute of Technology |
publishDate |
2018 |
url |
http://hdl.handle.net/1721.1/119515 |
work_keys_str_mv |
AT consulnatasha learninghowtoextractinformationfromscanneddocuments |
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1719029287668940800 |