Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
533034 | Pattern Recognition | 2004 | 11 Pages |
Abstract
In off-line handwriting recognition, classifiers based on hidden Markov models (HMMs) have become very popular. However, while there exist well-established training algorithms which optimize the transition and output probabilities of a given HMM architecture, the architecture itself, and in particular the number of states, must be chosen “by hand”. Also the number of training iterations and the output distributions need to be defined by the system designer. In this paper we examine several optimization strategies for an HMM classifier that works with continuous feature values. The proposed optimization strategies are evaluated in the context of a handwritten word recognition task.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Simon Günter, Horst Bunke,