Impact Analysis of OCR Quality on Research Tasks in Digital Archives

Humanities scholars increasingly rely on digital archives for their research in place of time-consuming visits to physical archives. This shift in research methodology has the hidden cost of working with digi- tally processed historical documents: how much trust can a scholar place in noisy representations of source texts? In a series of interviews with historians about their use of digital archives, we found that scholars are aware that optical character recognition (OCR) errors may bias their results. They were, however, unable to quantify this bias or to indicate what information they would need to estimate it. Based on the interviews and a literature study, we provide a classification scheme relating schol- arly research tasks to their specific OCR-induced uncertainty and the data required for more reliable uncertainty estimations. We conducted a use case study on a national newspaper archive with example research tasks. From this we learned what data is typically available in digital archives and how it could be used to reduce and/or assess the uncer- tainty in result sets. We conclude that the current knowledge situation on the users’ side as well as on the tool makers and data providers’ side is insufficient and needs further research to be improved.