Writer adaptation techniques in HMM based Off-Line Cursive Script Recognition
| Type of publication: | Journal paper |
| Citation: | vincia01art |
| Journal: | Pattern Recognition Letters |
| Volume: | 23 |
| Number: | 8 |
| Year: | 2002 |
| Note: | IDIAP-RR 01-15 |
| Crossref: | vincia01a: |
| Abstract: | 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. |
| Userfields: | ipdmembership={vision}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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