Hidden Markov Models Combining Discrete Symbols and Continuous Attributes in Handwriting Recognition

Authors:
Hanhong Xue;Venu Govindaraju
Affiliations:
-;IEEE
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2006

Citing 6
Cited 3

Handwritten Word Recognition Using Segmentation-Free Hidden Markov Modeling and Segmentation-Based Dynamic Programming Techniques

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A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications

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An Off-Line Cursive Handwriting Recognition System

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An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition

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Probabilistic Model for Segmentation Based Word Recognition with Lexicon

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Variable duration hidden Markov model and morphological segmentation for handwritten word recognition

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Using dynamic time warping for online temporal fusion in multisensor systems

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Phrase-based correction model for improving handwriting recognition accuracies

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Zero knowledge hidden Markov model inference

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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.