%0 Journal Article
%T Handwritten word-spotting using hidden Markov models and universal vocabularies
%A Jose Antonio Rodriguez
%A Florent Perronnin
%J Pattern Recognition
%D 2009
%V 42
%N 9
%I Elsevier
%@ 0031-3203
%F Jose Antonio Rodriguez2009
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1053), last updated on Thu, 19 Dec 2013 12:26:24 +0100
%X Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians.
%K Word-spotting
%K Hidden Markov model
%K Score normalization
%K Universal vocabulary
%K Handwriting recognition
%U http://dx.doi.org/10.1016/j.patcog.2009.02.005
%P 2103-2116