PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Embedded Bernoulli Mixture HMMs for Handwritten Word Recognition
Alfons Juan and Adrià Giménez
In: 10th International Conference on Document Analysis and Recognition, 26-29 July 2009, Spain.

Abstract

Hidden Markov Models (HMMs) are now widely used in off-line handwritten word recognition. As in speech recognition, they are usually built from shared, embedded HMMs at symbol level, in which state-conditional probability density functions are modelled with Gaussian mixtures. In contrast to speech recognition, however, it is unclear which kind of real-valued features should be used and, indeed, very different features sets are in use today. In this paper, we propose to by-pass feature extraction and directly fed columns of raw, binary image pixels into embedded Bernoulli mixture HMMs, that is, embedded HMMs in which the emission probabilities are modelled with Bernoulli mixtures. The idea is to ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. Empirical results are reported in which similar results are obtained with both Bernoulli and Gaussian mixtures, though Bernoulli mixtures are much simpler.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:5660
Deposited By:Alfons Juan
Deposited On:08 March 2010