@inproceedings{Rodriguez:08b,
	abstract = {Handwritten word spotting (HWS) is traditionally performed as an image matching task between one or multiple query images and a set of word images in a document. In this article, we address the word spotting problem as a hidden Markov model (HMM) word verification problem and demonstrate the importance of score normalization for improving detection performance. Our main contribution is the introduction of a novel score normalization technique in which the conventional HMM filler model is simplified by using a Gaussian mixture model (GMM). 
The accuracy of the proposed score normalization is on par with the traditional HMM-based score normalization approaches 
but it has a lower computational cost.
We also identify an interesting special case, the semi-continuous HMM, where the proposed score normalization formalism fits very elegantly and comes at a negligible cost.},
	author = {Rodriguez, Jose A. and Perronnin, Florent},
	booktitle = {Proceedings of the 1st International Conference on Handwriting Recognition (ICFHR'08)},
	location = {Montreal, Canada},
	month = {August},
	title = {Score normalization for HMM-based word spotting using a universal background model},
	year = {2008}
}