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Segmental K-Means Learning with Mixture Distribution for HMM Based Handwriting Recognition

  • Tapan Kumar Bhowmik
  • Jean-Paul van Oosten
  • Lambert Schomaker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

This paper investigates the performance of hidden Markov models (HMMs) for handwriting recognition. The Segmental K-Means algorithm is used for updating the transition and observation probabilities, instead of the Baum-Welch algorithm. Observation probabilities are modelled as multi-variate Gaussian mixture distributions. A deterministic clustering technique is used to estimate the initial parameters of an HMM. Bayesian information criterion (BIC) is used to select the topology of the model. The wavelet transform is used to extract features from a grey-scale image, and avoids binarization of the image.

Keywords

Hide Markov Model Bayesian Information Criterion Mixture Component Mixture Distribution Handwriting Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tapan Kumar Bhowmik
    • 1
  • Jean-Paul van Oosten
    • 1
  • Lambert Schomaker
    • 1
  1. 1.Faculty of Mathematics and Natural SciencesUniversity of GroningenNetherlands

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