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

Vinciarelli, A. and Bengio, S. (2002) Writer adaptation techniques in HMM based Off-Line Cursive Script Recognition. Pattern Recognition Letters, 23(8), pp. 905-916. (doi: 10.1016/S0167-8655(02)00021-1)

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Publisher's URL: http://dx.doi.org/10.1016/S0167-8655(02)00021-1


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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Vinciarelli, Professor Alessandro
Authors: Vinciarelli, A., and Bengio, S.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Pattern Recognition Letters
Publisher:Elsevier BV
ISSN (Online):1872-7344
Published Online:25 January 2002

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