A Maximum-Likelihood Approach to Segmentation-Based Recognition of Unconstrained Handwriting Text

@inproceedings{Senda2001AMA,
  title={A Maximum-Likelihood Approach to Segmentation-Based Recognition of Unconstrained Handwriting Text},
  author={Shuji Senda and Keiji Yamada},
  booktitle={ICDAR},
  year={2001}
}
In this paper, we propose a maximum-likelihood approach to segmentation-based recognition of unconstrained handwriting text. The segnientation scores and recognition scores are transformed into posterior probabilities, and the likelihood function which is composed of both these probabilities and character n-gram probabilities is derived from the Bayesian theorem. The recognition result which maximizes the function can be obtained by Viterbi search. Experiments have shown that the proposed… CONTINUE READING

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