  Optical character recognition (OCR) for historical documents is a complex
procedure subject to a unique set of material issues, including inconsistencies
in typefaces and low quality scanning. Consequently, even the most
sophisticated OCR engines produce errors. This paper reports on a tool built
for postediting the output of Tesseract, more specifically for correcting
common errors in digitized historical documents. The proposed tool suggests
alternatives for word forms not found in a specified vocabulary. The assumed
error is replaced by a presumably correct alternative in the post-edition based
on the scores of a Language Model (LM). The tool is tested on a chapter of the
book An Essay Towards Regulating the Trade and Employing the Poor of this
Kingdom (Cary ,1719). As demonstrated below, the tool is successful in
correcting a number of common errors. If sometimes unreliable, it is also
transparent and subject to human intervention.
