Semi-supervised Online Learning of Handwritten Characters Using a Bayesian Classifier

@article{Kunwar2013SemisupervisedOL,
  title={Semi-supervised Online Learning of Handwritten Characters Using a Bayesian Classifier},
  author={Rituraj Kunwar and Umapada Pal and Michael Blumenstein},
  journal={2013 2nd IAPR Asian Conference on Pattern Recognition},
  year={2013},
  pages={717-721}
}
This paper addresses the problem of creating a handwritten character recognizer, which makes use of both labelled and unlabelled data to learn continuously over time to make the recognisor adaptable. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for better parameter estimation, especially when labelled data is scarce and expensive unlike unlabelled data… CONTINUE READING

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