Classification of crowdsourced text correction

Megha Gupta; Haimonti Dutta; Brian Geiger

doi:10.1145/2732587.2732619

Classification of crowdsourced text correction

Megha Gupta
, Haimonti Dutta
, Brian Geiger

Management Science and Systems

Indraprastha Institute of Information Technology Delhi
University of California at Riverside
Columbia University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Optical Character Recognition (OCR) is a commonly used technique for digitizing printed material enabling them to be displayed online, searched and used in text mining applications. The text generated from OCR devices is often garbled due to variations in quality of the input paper, size and style of the font and column layout. This adversely affects retrieval effectiveness and hence techniques for cleaning the garbled text need to be improvised.This prototype system is expected to be deployed on historical newspaper archives that make extensive use of user text corrections.

Original language	English
Title of host publication	Proceedings of the 2nd ACM IKDD Conference on Data Sciences, CoDS 2015
Publisher	Association for Computing Machinery
Pages	142-143
Number of pages	2
ISBN (Electronic)	9781450334365
DOIs	https://doi.org/10.1145/2732587.2732619
State	Published - Mar 18 2015
Event	2nd ACM IKDD Conference on Data Sciences, CoDS 2015 - Bangalore, India Duration: Mar 18 2015 → Mar 21 2015

Publication series

Name	ACM International Conference Proceeding Series
Volume	18-21-March-2015

Conference

Conference	2nd ACM IKDD Conference on Data Sciences, CoDS 2015
Country/Territory	India
City	Bangalore
Period	03/18/15 → 03/21/15

Access to Document

10.1145/2732587.2732619

Cite this

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title = "Classification of crowdsourced text correction",

abstract = "Optical Character Recognition (OCR) is a commonly used technique for digitizing printed material enabling them to be displayed online, searched and used in text mining applications. The text generated from OCR devices is often garbled due to variations in quality of the input paper, size and style of the font and column layout. This adversely affects retrieval effectiveness and hence techniques for cleaning the garbled text need to be improvised.This prototype system is expected to be deployed on historical newspaper archives that make extensive use of user text corrections.",

author = "Megha Gupta and Haimonti Dutta and Brian Geiger",

year = "2015",

month = mar,

day = "18",

doi = "10.1145/2732587.2732619",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery ",

pages = "142--143",

booktitle = "Proceedings of the 2nd ACM IKDD Conference on Data Sciences, CoDS 2015",

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Gupta, M, Dutta, H & Geiger, B 2015, Classification of crowdsourced text correction. in Proceedings of the 2nd ACM IKDD Conference on Data Sciences, CoDS 2015. ACM International Conference Proceeding Series, vol. 18-21-March-2015, Association for Computing Machinery , pp. 142-143, 2nd ACM IKDD Conference on Data Sciences, CoDS 2015, Bangalore, India, 03/18/15. https://doi.org/10.1145/2732587.2732619

Classification of crowdsourced text correction. / Gupta, Megha; Dutta, Haimonti; Geiger, Brian.
Proceedings of the 2nd ACM IKDD Conference on Data Sciences, CoDS 2015. Association for Computing Machinery , 2015. p. 142-143 (ACM International Conference Proceeding Series; Vol. 18-21-March-2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Geiger, Brian

PY - 2015/3/18

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N2 - Optical Character Recognition (OCR) is a commonly used technique for digitizing printed material enabling them to be displayed online, searched and used in text mining applications. The text generated from OCR devices is often garbled due to variations in quality of the input paper, size and style of the font and column layout. This adversely affects retrieval effectiveness and hence techniques for cleaning the garbled text need to be improvised.This prototype system is expected to be deployed on historical newspaper archives that make extensive use of user text corrections.

AB - Optical Character Recognition (OCR) is a commonly used technique for digitizing printed material enabling them to be displayed online, searched and used in text mining applications. The text generated from OCR devices is often garbled due to variations in quality of the input paper, size and style of the font and column layout. This adversely affects retrieval effectiveness and hence techniques for cleaning the garbled text need to be improvised.This prototype system is expected to be deployed on historical newspaper archives that make extensive use of user text corrections.

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U2 - 10.1145/2732587.2732619

DO - 10.1145/2732587.2732619

M3 - Conference contribution

AN - SCOPUS:84958531177

T3 - ACM International Conference Proceeding Series

SP - 142

EP - 143

BT - Proceedings of the 2nd ACM IKDD Conference on Data Sciences, CoDS 2015

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T2 - 2nd ACM IKDD Conference on Data Sciences, CoDS 2015

Y2 - 18 March 2015 through 21 March 2015

ER -

Classification of crowdsourced text correction

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