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A maximum-likelihood approach to segmentation-based recognition of unconstrained handwriting text

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2 Author(s)
Senda, S. ; Comput. & Commun. Media Res., NEC Corp., Nara, Japan ; Yamada, K.

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 online Japanese text

Published in:

Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on

Date of Conference:

2001