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Utilizing big data in identification...

Agarwal, Shivam.

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Utilizing big data in identification and correction of OCR errors.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Utilizing big data in identification and correction of OCR errors./
Author:	Agarwal, Shivam.
Description:	63 p.
Notes:	Source: Masters Abstracts International, Volume: 52-03.
Contained By:	Masters Abstracts International52-03(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1548152
ISBN:	9781303530371

Utilizing big data in identification and correction of OCR errors.
Agarwal, Shivam.

Utilizing big data in identification and correction of OCR errors. - 63 p.

Source: Masters Abstracts International, Volume: 52-03.

Thesis (M.S.C.S.)--University of Nevada, Las Vegas, 2013.

In this thesis, we report on our experiments for detection and correction of OCR errors with web data. More specifically, we utilize Google search to access the big data resources available to identify possible candidates for correction. We then use a combination of the Longest Common Subsequences (LCS) and Bayesian estimates to automatically pick the proper candidate.

ISBN: 9781303530371Subjects--Topical Terms:

626642
Computer Science.

Utilizing big data in identification and correction of OCR errors.
LDR:01616nam a2200301 4500 001 1966786
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008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303530371
035 $a (MiAaPQ)AAI1548152
035 $a AAI1548152
040 $a MiAaPQ $c MiAaPQ
100 1 $a Agarwal, Shivam. $3 2103656
245 1 0 $a Utilizing big data in identification and correction of OCR errors.
300 $a 63 p.
500 $a Source: Masters Abstracts International, Volume: 52-03.
500 $a Adviser: Kazem Taghva.
502 $a Thesis (M.S.C.S.)--University of Nevada, Las Vegas, 2013.
520 $a In this thesis, we report on our experiments for detection and correction of OCR errors with web data. More specifically, we utilize Google search to access the big data resources available to identify possible candidates for correction. We then use a combination of the Longest Common Subsequences (LCS) and Bayesian estimates to automatically pick the proper candidate.
520 $a Our experimental results on a small set of historical newspaper data show a recall and precision of 51% and 100%, respectively. The work in this thesis further provides a detailed classification and analysis of all errors. In particular, we point out the shortcomings of our approach in its ability to suggest proper candidates to correct the remaining errors.
590 $a School code: 0506.
650 4 $a Computer Science. $3 626642
650 4 $a Information Technology. $3 1030799
650 4 $a Web Studies. $3 1026830
690 $a 0984
690 $a 0489
690 $a 0646
710 2 $a University of Nevada, Las Vegas. $b Computer Science. $3 1680278
773 0 $t Masters Abstracts International $g 52-03(E).
790 $a 0506
791 $a M.S.C.S.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1548152