  In the absence of ground truth it is not possible to automatically determine
the exact spectrum and occurrences of OCR errors in an OCR'ed text. Yet, for
interactive postcorrection of OCR'ed historical printings it is extremely
useful to have a statistical profile available that provides an estimate of
error classes with associated frequencies, and that points to conjectured
errors and suspicious tokens. The method introduced in Reffle (2013) computes
such a profile, combining lexica, pattern sets and advanced matching techniques
in a specialized Expectation Maximization (EM) procedure. Here we improve this
method in three respects: First, the method in Reffle (2013) is not adaptive:
user feedback obtained by actual postcorrection steps cannot be used to compute
refined profiles. We introduce a variant of the method that is open for
adaptivity, taking correction steps of the user into account. This leads to
higher precision with respect to recognition of erroneous OCR tokens. Second,
during postcorrection often new historical patterns are found. We show that
adding new historical patterns to the linguistic background resources leads to
a second kind of improvement, enabling even higher precision by telling
historical spellings apart from OCR errors. Third, the method in Reffle (2013)
does not make any active use of tokens that cannot be interpreted in the
underlying channel model. We show that adding these uninterpretable tokens to
the set of conjectured errors leads to a significant improvement of the recall
for error detection, at the same time improving precision.
