Evaluation of incremental learning algorithms for HMM in the recognition of alphanumeric characters

Authors:
Paulo R. Cavalin;Robert Sabourin;Ching Y. Suen;Alceu S. Britto Jr.
Affiliations:
ícole de Technologie Supérieure, 1100 Notre-dame ouest, Montréal, QC, Canada H3C-1K3;ícole de Technologie Supérieure, 1100 Notre-dame ouest, Montréal, QC, Canada H3C-1K3;Centre for Pattern Recognition and Machine Intelligence (CENPARMI), Concordia University, 1455 de Maisonneuve Blvd West, Montréal, QC, Canada H3G-1M8;Pontifícia Universidade Católica do Paraná, Rua Imaculada Conceição, 1155 - Curitiba, PR 80.215-901, Brazil
Venue:
Pattern Recognition
Year:
2009

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Abstract

We present an evaluation of incremental learning algorithms for the estimation of hidden Markov model (HMM) parameters. The main goal is to investigate incremental learning algorithms that can provide as good performances as traditional batch learning techniques, but incorporating the advantages of incremental learning for designing complex pattern recognition systems. Experiments on handwritten characters have shown that a proposed variant of the ensemble training algorithm, employing ensembles of HMMs, can lead to very promising performances. Furthermore, the use of a validation dataset demonstrated that it is possible to reach better performances than the ones presented by batch learning.