%0 Conference Proceedings
%T Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents
%A L. Rothacker
%A Marçal Rusiñol
%A G.A. Fink
%B 12th International Conference on Document Analysis and Recognition
%D 2013
%@ 1520-5363
%F L. Rothacker2013
%O DAG
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2344), last updated on Thu, 10 Nov 2016 12:10:29 +0100
%X 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.
%U http://refbase.cvc.uab.es/files/RRF2013.pdf
%U http://dx.doi.org/10.1109/ICDAR.2013.264
%P 1305-1309