  The focus of our paper is the identification and correction of non-word
errors in OCR text. Such errors may be the result of incorrect insertion,
deletion, or substitution of a character, or the transposition of two adjacent
characters within a single word. Or, it can be the result of word boundary
problems that lead to run-on errors and incorrect-split errors. The traditional
N-gram correction methods can handle single-word errors effectively. However,
they show limitations when dealing with split and merge errors. In this paper,
we develop an unsupervised method that can handle both errors. The method we
develop leads to a sizable improvement in the correction rates. This tutorial
paper addresses very difficult word correction problems - namely incorrect
run-on and split errors - and illustrates what needs to be considered when
addressing such problems. We outline a possible approach and assess its success
on a limited study.
