Enhanced continuous sign language recognition using PCA and neural network features

@article{Gweth2012EnhancedCS,
  title={Enhanced continuous sign language recognition using PCA and neural network features},
  author={Yannick L. Gweth and Christian Plahl and Hermann Ney},
  journal={2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
  year={2012},
  pages={55-60}
}
In this work a Gaussian Hidden Markov Model (GHMM) based automatic sign language recognition system is built on the SIGNUM database. The system is trained on appearance-based features as well as on features derived from a multilayer perceptron (MLP). Appearance-based features are directly extracted from the original images without any colored gloves or sensors. The posterior estimates are derived from a neural network. Whereas MLP based features are well-known in speech and optical character… CONTINUE READING

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Key Quantitative Results

  • The MLP based features improve the word error rate (WER) of the system from 16% to 13% compared to the appearance-based features.
  • By means of the combination technique, we could improve the word error rate of our best system by more than 8% relative and outperform the best published results on this database by about 6% relative.

Citations

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Re-Sign: Re-Aligned End-to-End Sequence Modelling with Deep Recurrent CNN-HMMs

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017

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