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

ICDAR-1（2001）

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摘要

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

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关键词

likelihood function,experiments,unconstrained handwriting recognition,bayesian theorem,posterior probabilities,unconstrained handwriting text,bayes methods,maximum likelihood estimation,segmentation-based recognition,image segmentation,ocr,viterbi search,character n-gram probability,recognition result,maximum-likelihood approach,recognition score,text recognition,handwritten character recognition,optical character recognition,on-line japanese text,character n-gram probabilities,proposed likelihood function,online japanese text,document image processing,probability,flowcharts,bayesian methods,dictionaries,lattices,national electric code,maximum likelihood,handwriting recognition,posterior probability,viterbi algorithm

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