Sciweavers

Share
PRL
2002

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

10 years 28 days ago
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. 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.
Alessandro Vinciarelli, Samy Bengio
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 2002
Where PRL
Authors Alessandro Vinciarelli, Samy Bengio
Comments (0)
books