  Digitization of historical documents is a challenging task in many digital
humanities projects. A popular approach for digitization is to scan the
documents into images, and then convert images into text using Optical
Character Recognition (OCR) algorithms. However, the outcome of OCR processing
of historical documents is usually inaccurate and requires post-processing
error correction. This study investigates how crowdsourcing can be utilized to
correct OCR errors in historical text collections, and which crowdsourcing
methodology is the most effective in different scenarios and for various
research objectives. A series of experiments with different micro-task's
structures and text lengths was conducted with 753 workers on the Amazon's
Mechanical Turk platform. The workers had to fix OCR errors in a selected
historical text. To analyze the results, new accuracy and efficiency measures
have been devised. The analysis suggests that in terms of accuracy, the optimal
text length is medium (paragraph-size) and the optimal structure of the
experiment is two-phase with a scanned image. In terms of efficiency, the best
results were obtained when using longer text in the single-stage structure with
no image. The study provides practical recommendations to researchers on how to
build the optimal crowdsourcing task for OCR post-correction. The developed
methodology can also be utilized to create golden standard historical texts for
automatic OCR post-correction. This is the first attempt to systematically
investigate the influence of various factors on crowdsourcing-based OCR
post-correction and propose an optimal strategy for this process.
