Writer adaptation techniques in HMM based Off-Line Cursive Script Recognition

This work presents the application of HMM adaptation techniques to the problem of Off-Line Cursive Script Recognition. Instead of 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.


Published in:
Pattern Recognition Letters, 23, 8, 905-916
Year:
2002
Keywords:
Note:
IDIAP-RR 01-15
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

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