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Writer adaptation techniques in HMM based Off-Line Cursive Script Recognition
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DOI：10.1016/S0167-8655(02)00021-1.png)
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Abstract
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This work presents the application of HMM adaptation techniques to the problem of Off-Line Cursive Script Recognition. Rather than training a new model for each writer, one first creates a unique model with a mixed database and then adapts it for each different writer using his own small dataset. Experiments on a publicly available benchmark database show that an adapted system has an accuracy higher than 80% even when less than 30 word samples are used during adaptation, while a system trained using the data of the single writer only needs at least 200 words in order to achieve the same performance as the adapted models. (C) 2002 Elsevier Science B.V. All rights reserved.
Keywords:
Off-Line Cursive Script Recognition
HMM Bayesian adaptation
HMM maximum likelihood adaptation
HMM maximum A posteriori adaptation
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