Combining model-based and discriminative classifiers: application to handwritten character recognition

@article{Prevost2003CombiningMA,
  title={Combining model-based and discriminative classifiers: application to handwritten character recognition},
  author={Lionel Prevost and Christian Michel-Sendis and Alvaro Moises and Lo{\"i}c Oudot and Maurice Milgram},
  journal={Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.},
  year={2003},
  pages={31-35 vol.1}
}
Handwriting recognition is such a complex classification problem that it is quite usual now to make co-operate several classification methods at the pre-processing stage or at the classification stage. In this paper, we present an original two stages recognizer. The first stage is a model-based classifier that stores an exhaustive set of character models. The second stage is a discriminative classifier that separates the most ambiguous pairs of classes. This hybrid architecture is based on the… CONTINUE READING

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Key Quantitative Results

  • Experiments on Unipen database show a 30% improvement on a 62 classes recognition problem.

Citations

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SHOWING 1-10 OF 18 CITATIONS

A Hybrid Recogniser for Handwritten Symbols Based on Fuzzy Logic and Self-Organizing Maps

  • 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)
  • 2006
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