Since their first inception, automatic reading systems have
evolved substantially, yet the recognition of handwriting remains
an open research problem due to its substantial variation in
appearance. With the introduction of Markovian models to the field,
a promising modeling and recognition paradigm was established for
automatic handwriting recognition. However, no standard procedures
for building Markov model-based recognizers have yet been
established. This text provides a comprehensive overview of the
application of Markov models in the field of handwriting
recognition, covering both hidden Markov models and Markov-chain or
n-gram models. First, the text introduces the typical architecture
of a Markov model-based handwriting recognition system, and
familiarizes the reader with the essential theoretical concepts
behind Markovian models. Then, the text reviews proposed solutions
in the literature for open problems in applying Markov model-based
approaches to automatic handwriting recognition.
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