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

Vinciarelli, Alessandro  
•
Bengio, Samy  
2002
Proceedings of 8$^th$ International Conference on Frontiers on Handwriting Recognition
8$^{th}$ International Conference on Frontiers on Handwriting 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.

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