A Bayesian Framework for Fusing Multiple Word Knowledge Models in Videotext Recognition

@inproceedings{Zhang2003ABF,
  title={A Bayesian Framework for Fusing Multiple Word Knowledge Models in Videotext Recognition},
  author={DongQing Zhang and Shih-Fu Chang},
  booktitle={CVPR},
  year={2003}
}
Videotext recognition is challenging due to low resolution, diverse fonts/styles, and cluttered background. Past methods enhanced recognition by using multiple frame averaging, image interpolation and lexicon correction, but recognition using multi-modality language models has not been explored. In this paper, we present a formal Bayesian framework for videotext recognition by combining multiple knowledge using mixture models, and describe a learning approach based on Expectation-Maximization… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 15 references

A Multi-Model Bayesian Framework for Videotext Recognition, ADVENT

D. Zhang, S. F. Chang
Technical Report • 2003
View 1 Excerpt

End-to-end videotext recognition for multimedia content analysis

IEEE International Conference on Multimedia and Expo, 2001. ICME 2001. • 2001
View 1 Excerpt

Pattern Classification

R. O. Duda, P. E. Hart, D. G. Stock
Wiley-Interscience, New York, NY, 2 ed. • 2000
View 2 Excerpts