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Handwritten historical document analysis, recognition, and retrieval = state of the art and future trends /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Handwritten historical document analysis, recognition, and retrieval/ [edited by] Andreas Fischer, Marcus Liwicki, Rolf Ingold.
Reminder of title:	state of the art and future trends /
other author:	Fischer, Andreas.
Published:	Singapore :World Scientific, : c2021.,
Description:	1 online resource (xiii, 254 p.)
Notes:	Inlcudes index.
Subject:	Optical pattern recognition. -
Online resource:	https://www.worldscientific.com/worldscibooks/10.1142/11353#t=toc
ISBN:	9789811203244

Handwritten historical document analysis, recognition, and retrieval = state of the art and future trends /
Handwritten historical document analysis, recognition, and retrievalstate of the art and future trends /[electronic resource] :[edited by] Andreas Fischer, Marcus Liwicki, Rolf Ingold. - 1st ed. - Singapore :World Scientific,c2021. - 1 online resource (xiii, 254 p.) - Series in machine perception and artificial intelligence ;v. 89. - Series in machine perception and artificial intelligence ;v. 73.

Inlcudes index.

"In recent years, libraries and archives all around the world have increased their efforts to digitize historical manuscripts. To integrate the manuscripts into digital libraries, pattern recognition and machine learning methods are needed to extract and index the contents of the scanned images. The unique compendium describes the outcome of the HisDoc research project, a pioneering attempt to study the whole processing chain of layout analysis, handwriting recognition, and retrieval of historical manuscripts. This description is complemented with an overview of other related research projects, in order to convey the current state of the art in the field and outline future trends. This must-have volume is a relevant reference work for librarians, archivists and computer scientists"--Publisher's website.

Mode of access: World Wide Web.

ISBN: 9789811203244Subjects--Topical Terms:

557494
Optical pattern recognition.

LC Class. No.: TA1650 / .H36 2021

Dewey Class. No.: 006.42

Handwritten historical document analysis, recognition, and retrieval = state of the art and future trends /
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245 0 0 $a Handwritten historical document analysis, recognition, and retrieval $h [electronic resource] : $b state of the art and future trends / $c [edited by] Andreas Fischer, Marcus Liwicki, Rolf Ingold.
250 $a 1st ed.
260 $a Singapore : $b World Scientific, $c c2021.
300 $a 1 online resource (xiii, 254 p.)
490 1 $a Series in machine perception and artificial intelligence ; $v v. 89
500 $a Inlcudes index.
520 $a "In recent years, libraries and archives all around the world have increased their efforts to digitize historical manuscripts. To integrate the manuscripts into digital libraries, pattern recognition and machine learning methods are needed to extract and index the contents of the scanned images. The unique compendium describes the outcome of the HisDoc research project, a pioneering attempt to study the whole processing chain of layout analysis, handwriting recognition, and retrieval of historical manuscripts. This description is complemented with an overview of other related research projects, in order to convey the current state of the art in the field and outline future trends. This must-have volume is a relevant reference work for librarians, archivists and computer scientists"--Publisher's website.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Description based on print version record.
650 0 $a Optical pattern recognition. $3 557494
650 0 $a Text processing (Computer science) $3 528615
650 0 $a Document imaging systems. $3 793269
650 0 $a Image analysis. $3 578867
650 0 $a Digital libraries. $3 561243
700 1 $a Fischer, Andreas. $e editor. $3 1297961
700 1 $a Liwicki, Marcus. $3 713841
700 1 $a Ingold, Rolf. $3 1342615
830 0 $a Series in machine perception and artificial intelligence ; $v v. 73 $3 831270
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/11353#t=toc