## 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:
Proceedings of 8$^th$ International Conference on Frontiers on Handwriting Recognition, 287-291
Presented at:
Proceedings of 8$^{th}$ International Conference on Frontiers on Handwriting Recognition
Year:
2002
Publisher:
Keywords:
Note:
IDIAP-RR 01-15
Laboratories: