  Linked Data is used in various fields as a new way of structuring and
connecting data. Cultural heritage institutions have been using linked data to
improve archival descriptions and facilitate the discovery of information. Most
archival records have digital representations of physical artifacts in the form
of scanned images that are non-machine-readable. Optical Character Recognition
(OCR) recognizes text in images and translates it into machine-encoded text.
This paper evaluates the impact of image processing methods and parameter
tuning in OCR applied to typewritten cultural heritage documents. The approach
uses a multi-objective problem formulation to minimize Levenshtein edit
distance and maximize the number of words correctly identified with a
non-dominated sorting genetic algorithm (NSGA-II) to tune the methods'
parameters. Evaluation results show that parameterization by digital
representation typology benefits the performance of image pre-processing
algorithms in OCR. Furthermore, our findings suggest that employing image
pre-processing algorithms in OCR might be more suitable for typologies where
the text recognition task without pre-processing does not produce good results.
In particular, Adaptive Thresholding, Bilateral Filter, and Opening are the
best-performing algorithms for the theatre plays' covers, letters, and overall
dataset, respectively, and should be applied before OCR to improve its
performance.
