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A Visual Focus on Form Understanding.
Record Type:
Electronic resources : Monograph/item
Title/Author:
A Visual Focus on Form Understanding./
Author:
Davis, Brian Lafayette.
Description:
1 online resource (141 pages)
Notes:
Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:
Dissertations Abstracts International84-03B.
Subject:
Language. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29282743click for full text (PQDT)
ISBN:
9798845455642
A Visual Focus on Form Understanding.
Davis, Brian Lafayette.
A Visual Focus on Form Understanding.
- 1 online resource (141 pages)
Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Thesis (Ph.D.)--Brigham Young University, 2022.
Includes bibliographical references
Paper forms are a commonly used format for collecting information, including information that ultimately will be added to a digital database. This work focuses on the automatic extraction of information from form images. It examines what can be achieved at parsing forms without any textual information. The resulting model, FUDGE, shows that computer vision alone is reasonably successful at the problem. Drawing from the strengths and weaknesses of FUDGE, this work also introduces a novel model, Dessurt, for end-to-end document understanding. Dessurt performs text recognition implicitly and is capable of outputting arbitrary text, making it a more flexible document processing model than prior methods. Dessurt is capable of parsing the entire contents of a form image into a structured format directly, achieving better performance than FUDGE at this task. Also included is a technique to generate synthetic handwriting, which provides synthetic training data for Dessurt.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023
Mode of access: World Wide Web
ISBN: 9798845455642Subjects--Topical Terms:
643551
Language.
Index Terms--Genre/Form:
542853
Electronic books.
A Visual Focus on Form Understanding.
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A Visual Focus on Form Understanding.
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Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
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Advisor: Morse, Bryan;Farrell, Ryan;David, David;Mercer, Eric.
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Thesis (Ph.D.)--Brigham Young University, 2022.
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Includes bibliographical references
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Paper forms are a commonly used format for collecting information, including information that ultimately will be added to a digital database. This work focuses on the automatic extraction of information from form images. It examines what can be achieved at parsing forms without any textual information. The resulting model, FUDGE, shows that computer vision alone is reasonably successful at the problem. Drawing from the strengths and weaknesses of FUDGE, this work also introduces a novel model, Dessurt, for end-to-end document understanding. Dessurt performs text recognition implicitly and is capable of outputting arbitrary text, making it a more flexible document processing model than prior methods. Dessurt is capable of parsing the entire contents of a form image into a structured format directly, achieving better performance than FUDGE at this task. Also included is a technique to generate synthetic handwriting, which provides synthetic training data for Dessurt.
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click for full text (PQDT)
based on 0 review(s)
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