To read the full version of this content please select one of the options below:

Open source optical character recognition for historical research

Tobias Blanke (Centre for e‐Research, King's College London, London, United Kingdom)
Michael Bryant (Centre for e‐Research, King's College London, London, United Kingdom)
Mark Hedges (Centre for e‐Research, King's College London, London, United Kingdom)

Journal of Documentation

ISSN: 0022-0418

Article publication date: 31 August 2012

Abstract

Purpose

This paper aims to present an evaluation of open source OCR for supporting research on material in small‐ to medium‐scale historical archives.

Design/methodology/approach

The approach was to develop a workflow engine to support the easy customisation of the OCR process towards the historical materials using open source technologies. Commercial OCR often fails to deliver sufficient results here, as their processing is optimised towards large‐scale commercially relevant collections. The approach presented here allows users to combine the most effective parts of different OCR tools.

Findings

The authors demonstrate their application and its flexibility and present two case studies, which demonstrate how OCR can be embedded into wider digitally enabled historical research. The first case study produces high‐quality research‐oriented digitisation outputs, utilizing services that the authors developed to allow for the direct linkage of digitisation image and OCR output. The second case study demonstrates what becomes possible if OCR can be customised directly within a larger research infrastructure for history. In such a scenario, further semantics can be added easily to the workflow, enhancing the research browse experience significantly.

Originality/value

There has been little work on the use of open source OCR technologies for historical research. This paper demonstrates that the authors' workflow approach allows users to combine commercial engines' ability to read a wider range of character sets with the flexibility of open source tools in terms of customisable pre‐processing and layout analysis. All this can be done without the need to develop dedicated code.

Keywords

Citation

Blanke, T., Bryant, M. and Hedges, M. (2012), "Open source optical character recognition for historical research", Journal of Documentation, Vol. 68 No. 5, pp. 659-683. https://doi.org/10.1108/00220411211256021

Publisher

:

Emerald Group Publishing Limited

Copyright © 2012, Emerald Group Publishing Limited