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A Visual Focus on Form Understanding.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Visual Focus on Form Understanding./
作者:	Davis, Brian Lafayette.
面頁冊數:	1 online resource (141 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
標題:	Language. -
電子資源:	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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245 1 2 $a A Visual Focus on Form Understanding.
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500 $a Advisor: Morse, Bryan;Farrell, Ryan;David, David;Mercer, Eric.
502 $a Thesis (Ph.D.)--Brigham Young University, 2022.
504 $a Includes bibliographical references
520 $a 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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538 $a Mode of access: World Wide Web
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650 4 $a Government archives. $3 3559995
650 4 $a Optimization. $3 891104
650 4 $a Neural networks. $3 677449
650 4 $a Labeling. $3 3560710
650 4 $a Layouts. $3 3681514
650 4 $a Probability. $3 518898
650 4 $a Handwriting. $3 3559053
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650 4 $a Linguistics. $3 524476
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650 4 $a Mathematics. $3 515831
650 4 $a Political science. $3 528916
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773 0 $t Dissertations Abstracts International $g 84-03B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29282743 $z click for full text (PQDT)