%Aigaion2 BibTeX export from Idiap Publications
%Friday 05 December 2025 03:52:45 PM

@ARTICLE{vincia01art,
                      author = {Vinciarelli, Alessandro and Bengio, Samy},
                    projects = {Idiap},
                       title = {Writer adaptation techniques in {HMM} based Off-Line Cursive Script Recognition},
                     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.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2001/rr01-15.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2001/rr01-15.ps.gz},
ipdmembership={vision},
}



crossreferenced publications: 
@TECHREPORT{vincia01a,
                      author = {Vinciarelli, Alessandro and Bengio, Samy},
                    projects = {Idiap},
                       title = {Writer adaptation techniques in HMM based Off-Line Cursive Script Recognition},
                        type = {Idiap-RR},
                      number = {Idiap-RR-15-2001},
                        year = {2001},
                 institution = {IDIAP},
                    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.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2001/rr01-15.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2001/rr01-15.ps.gz},
ipdmembership={vision},
}