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ABSTRACT
Prior arts in handwritten word recognition model either discrete features or continuous features, but not both. This paper combines discrete symbols and continuous attributes into structural handwriting features and model, them by transition-emitting and state-emitting hidden Markov models. The models are rigorously defined and experiments have proven their effectiveness.
INDEX TERMS
Markov processes, handwriting analysis.
CITATION
Venu Govindaraju, Hanhong Xue, "Hidden Markov Models Combining Discrete Symbols and Continuous Attributes in Handwriting Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 458-462, March 2006, doi:10.1109/TPAMI.2006.55
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