About: Assessing the Impact of OCR Errors in Information Retrieval

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Assessing the Impact of OCR Errors in Information Retrieval
Creator	Lorentz, Gustavo Moreira, Viviane Torresan Bazzo, Guilherme Vargas, Danny
Source	PMC
abstract	A significant amount of the textual content available on the Web is stored in PDF files. These files are typically converted into plain text before they can be processed by information retrieval or text mining systems. Automatic conversion typically introduces various errors, especially if OCR is needed. In this empirical study, we simulate OCR errors and investigate the impact that misspelled words have on retrieval accuracy. In order to quantify such impact, errors were systematically inserted at varying rates in an initially clean IR collection. Our results showed that significant impacts are noticed starting at a 5% error rate. Furthermore, stemming has proven to make systems more robust to errors.
has issue date	2020-03-24(xsd:dateTime)
bibo:doi	10.1007/978-3-030-45442-5_13
has license	no-cc
sha1sum (hex)	e9bcae61af23dea7db0b4720b7804269e923f4cb
schema:url	https://doi.org/10.1007/978-3-030-45442-5_13
resource representing a document's title	Assessing the Impact of OCR Errors in Information Retrieval
has PubMed Central identifier	PMC7148068
schema:publication	Advances in Information Retrieval
resource representing a document's body	covid:e9bcae61af23dea7db0b4720b7804269e923f4cb#body_text
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