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

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ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Year:
2001

Citing 0
Cited 4

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Abstract

Abstract: In this paper, we propose a maximum-likelihood approach to segmentation-based recognition of unconstrained handwriting text. The segmentation 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 likelihood function is effective in the recognition of on-line Japanese text.