  This paper describes a number of fundamental and practical problems in the
application of hidden-Markov models and Bayes when applied to cursive-script
recognition. Several problems, however, will have an effect in other
application areas. The most fundamental problem is the propagation of error in
the product of probabilities. This is a common and pervasive problem which
deserves more attention. On the basis of Monte Carlo modeling, tables for the
expected relative error are given. It seems that it is distributed according to
a continuous Poisson distribution over log probabilities. A second essential
problem is related to the appropriateness of the Markov assumption. Basic tests
will reveal whether a problem requires modeling of the stochastics of
seriality, at all. Examples are given of lexical encodings which cover 95-99%
classification accuracy of a lexicon, with removed sequence information, for
several European languages. Finally, a summary of results on a non- Bayes,
non-Markov method in handwriting recognition are presented, with very
acceptable results and minimal modeling or training requirements using
nearest-mean classification.
