@InProceedings{L.Rothacker2013,
author="L. Rothacker
and Mar{\c{c}}al Rusi{\~n}ol
and G.A. Fink",
title="Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents",
booktitle="12th International Conference on Document Analysis and Recognition",
year="2013",
pages="1305--1309",
abstract="Recent HMM-based approaches to handwritten word spotting require large amounts of learning samples and mostly rely on a prior segmentation of the document. We propose to use Bag-of-Features HMMs in a patch-based segmentation-free framework that are estimated by a single sample. Bag-of-Features HMMs use statistics of local image feature representatives. Therefore they can be considered as a variant of discrete HMMs allowing to model the observation of a number of features at a point in time. The discrete nature enables us to estimate a query model with only a single example of the query provided by the user. This makes our method very flexible with respect to the availability of training data. Furthermore, we are able to outperform state-of-the-art results on the George Washington dataset.",
optnote="DAG",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2344), last updated on Thu, 10 Nov 2016 12:10:29 +0100",
issn="1520-5363",
doi="10.1109/ICDAR.2013.264",
file=":http://refbase.cvc.uab.es/files/RRF2013.pdf:PDF"
}