Segmental K-means learning with mixture distribution for HMM based handwriting recognition

Authors:
Tapan Kumar Bhowmik;Jean-Paul van Oosten;Lambert Schomaker
Affiliations:
Faculty of Mathematics and Natural Sciences, University of Groningen, Netherlands;Faculty of Mathematics and Natural Sciences, University of Groningen, Netherlands;Faculty of Mathematics and Natural Sciences, University of Groningen, Netherlands
Venue:
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Year:
2011

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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.