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publ. in Proc. of the 6th Int. Conf. on Document Analysis and Recognition (ICDAR) 2001, pp. 406411, 2001. ftp://ftp.informatik.uni-freiburg.de/papers/lmb/ba_bu_icdar01.pdf
 

Summary: publ. in Proc. of the 6th Int. Conf. on Document Analysis and Recognition (ICDAR) 2001, pp. 406­411, 2001.
ftp://ftp.informatik.uni-freiburg.de/papers/lmb/ba_bu_icdar01.pdf
Measuring HMM Similarity
with the Bayes Probability of Error
and its Application to Online Handwriting Recognition
Claus Bahlmann and Hans Burkhardt
Computer Science Department
Albert Ludwigs University Freiburg
79110 Freiburg, Germany
{bahlmann,burkhardt}@informatik.uni-freiburg.de
Abstract
We propose a novel similarity measure for Hidden
Markov Models (HMMs). This measure calculates the
Bayes probability of error for HMM state correspondences
and propagates it along the Viterbi path in a similar way
to the HMM Viterbi scoring. It can be applied as a tool
to interpret misclassifications, as a stop criterion in iter-
ative HMM training or as a distance measure for HMM
clustering. The similarity measure is evaluated in the con-
text of online handwriting recognition on lower case char-

  

Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung

 

Collections: Computer Technologies and Information Sciences