NDLI: A Hidden Markov Models combination framework for handwriting recognition

Content Provider	Springer Nature Link
Author	Artières, T. Gauthier, N. Gallinari, P. Dorizzi, B.
Copyright Year	2003
Abstract	We propose a general framework to combine multiple sequence classifiers working on different sequence representations of a given input. This framework, based on Multi-Stream Hidden Markov Models (MS-HMMs), allows the combination of multiple HMMs operating on partially asynchronous information streams. This combination may operate at different levels of modeling: from the feature level to the post-processing level. This framework is applied to on-line handwriting word recognition by combining temporal and spatial representation of the signal. Different combination schemes are compared experimentally on isolated character recognition and word recognition tasks, using the UNIPEN international database.
Starting Page	233
Ending Page	243
Page Count	11
File Format	PDF
ISSN	14332833
Journal	International Journal of Document Analysis and Recognition (IJDAR)
Volume Number	5
Issue Number	4
e-ISSN	14332825
Language	English
Publisher	Springer-Verlag
Publisher Date	2003-01-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Handwriting recognition Hidden Markov Models Combination Multi-Stream HMM On-line / off-line combination
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Computer Vision and Pattern Recognition Software

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A Hidden Markov Models combination framework for handwriting recognition