Hidden Markov Models (HMMs) are now widely used in off-line handwritten text 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. Good empirical results are reported on the well-known IAM database. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Giménez, A., & Juan, A. (2009). Embedded bernoulli mixture HMMs for continuous handwritten text recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5702 LNCS, pp. 197–204). https://doi.org/10.1007/978-3-642-03767-2_24
Mendeley helps you to discover research relevant for your work.