Writer adaptation techniques in HMM based off-line cursive script recognition

Authors:
Alessandro Vinciarelli;Samy Bengio
Affiliations:
IDIAP - Institut Dalle Molle d'Intelligence, Artificielle Perceptive, Rue du Simplon 4, CP 592, 1920 Martigny, Switzerland;IDIAP - Institut Dalle Molle d'Intelligence, Artificielle Perceptive, Rue du Simplon 4, CP 592, 1920 Martigny, Switzerland
Venue:
Pattern Recognition Letters
Year:
2002

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Abstract

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.