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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[Author vitae]
Affiliation:aÉcole de Technologie Supérieure, 1100 Notre-dame ouest, Montréal, QC, Canada H3C-1K3;bCentre for Pattern Recognition and Machine Intelligence (CENPARMI), Concordia University, 1455 de Maisonneuve Blvd West, Montréal, QC, Canada H3G-1M8;cPontifícia Universidade Católica do Paraná, Rua Imaculada Conceição, 1155 - Curitiba, PR 80.215-901, Brazil
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.
Keywords:Incremental learning  Hidden Markov models  Ensembles of classifiers  Handwriting recognition  Isolated digits  Uppercase letters
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