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

Caveats on Bayesian and hidden-Markov models (v2.8)