Impact of Crowdsourcing OCR Improvements on Retrievability Bias
Digitized document collections often suffer from OCR errors that may impact a document's readability and retrievability. We studied the effects of correcting OCR errors on the retrievability of documents in a historic newspaper corpus of a digital library. We computed retrievability scores for the uncorrected documents using queries from the library's search log, and found that the document OCR character error rate and retrievability score are strongly correlated. We computed retrievability scores for manually corrected versions of the same documents, and report on differences in their total sum, the overall retrievability bias, and the distribution of these changes over the documents, queries and query terms. For large collections, often only a fraction of the corpus is manually corrected. Using a mixed corpus, we assess how this mix affects the retrievability of the corrected and uncorrected documents. The correction of OCR errors increased the number of documents retrieved in all conditions. The increase contributed to a less biased retrieval, even when taking the potential lower ranking of uncorrected documents into account.
|Commit: Time Trails (P019) , Web Archives Retrieval Tools , A Europe-wide Interoperable Virtual Research Environment to Empower Multidisciplinary Research Communities and Accelerate Innovation and Collaboration|
|IEEE/ACM Joint Conference on Digital Libraries|
|Organisation||Human-Centered Data Analytics|
Traub, M.C, van Ossenbruggen, J.R, Samar, T, & Hardman, L. (2018). Impact of Crowdsourcing OCR Improvements on Retrievability Bias. In ACM International Conference Proceeding Series (pp. 29–36). doi:10.1145/3197026.3197046