Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition

Hanhong Xue; Venu Govindaraju

doi:10.1109/TPAMI.2006.55

Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition

Hanhong Xue
, Venu Govindaraju

Research and Economic Development

IBM

Research output: Contribution to journal › Article › peer-review

32 Scopus citations

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.

Original language	English
Pages (from-to)	458-462
Number of pages	5
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	28
Issue number	3
DOIs	https://doi.org/10.1109/TPAMI.2006.55
State	Published - Mar 2006

Keywords

Handwriting analysis
Markov processes

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10.1109/TPAMI.2006.55

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title = "Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition",

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.",

keywords = "Handwriting analysis, Markov processes",

author = "Hanhong Xue and Venu Govindaraju",

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KW - Handwriting analysis

KW - Markov processes

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JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

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Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition

Abstract

Keywords

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