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In-depth analysis of the impact of OCR errors on named entity recognition and linking

Published online by Cambridge University Press:  18 March 2022

Ahmed Hamdi Open the ORCID record for Ahmed Hamdi [Opens in a new window] ,
Elvys Linhares Pontes Open the ORCID record for Elvys Linhares Pontes [Opens in a new window] ,
Nicolas Sidere Open the ORCID record for Nicolas Sidere [Opens in a new window] ,
Mickaël Coustaty Open the ORCID record for Mickaël Coustaty [Opens in a new window]  and
Antoine Doucet Open the ORCID record for Antoine Doucet [Opens in a new window]
Ahmed Hamdi*
Affiliation:
University of La Rochelle, Laboratoire L3i, Faculté des Sciences et Technologies, Bâtiment Pascal, Avenue Michel Crépeau, 17042 La Rochelle, France
Elvys Linhares Pontes
Affiliation:
University of La Rochelle, Laboratoire L3i, Faculté des Sciences et Technologies, Bâtiment Pascal, Avenue Michel Crépeau, 17042 La Rochelle, France
Nicolas Sidere
Affiliation:
University of La Rochelle, Laboratoire L3i, Faculté des Sciences et Technologies, Bâtiment Pascal, Avenue Michel Crépeau, 17042 La Rochelle, France
Mickaël Coustaty
Affiliation:
University of La Rochelle, Laboratoire L3i, Faculté des Sciences et Technologies, Bâtiment Pascal, Avenue Michel Crépeau, 17042 La Rochelle, France
Antoine Doucet
Affiliation:
University of La Rochelle, Laboratoire L3i, Faculté des Sciences et Technologies, Bâtiment Pascal, Avenue Michel Crépeau, 17042 La Rochelle, France
*
*Corresponding author. E-mail: ahmed.hamdi@univ-lr.fr
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Abstract

Named entities (NEs) are among the most relevant type of information that can be used to properly index digital documents and thus easily retrieve them. It has long been observed that NEs are key to accessing the contents of digital library portals as they are contained in most user queries. However, most digitized documents are indexed through their optical character recognition (OCRed) version which include numerous errors. Although OCR engines have considerably improved over the last few years, OCR errors still considerably impact document access. Previous works were conducted to evaluate the impact of OCR errors on named entity recognition (NER) and named entity linking (NEL) techniques separately. In this article, we experimented with a variety of OCRed documents with different levels and types of OCR noise to assess in depth the impact of OCR on named entity processing. We provide a deep analysis of OCR errors that impact the performance of NER and NEL. We then present the resulting exhaustive study and subsequent recommendations on the adequate documents, the OCR quality levels, and the post-OCR correction strategies required to perform reliable NER and NEL.

Keywords

Named entity recognition and linkingDocument indexingInformation retrievalOptical character recognitionNeural networks

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Type
Article
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Natural Language Engineering , Volume 29 , Issue 2 , March 2023 , pp. 425 - 448
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© The Author(s), 2022. Published by Cambridge University Press

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