TY  - CONF
AU  - L. Rothacker
AU  - Marçal Rusiñol
AU  - G.A. Fink
A2  - ICDAR
PY  - 2013//
TI  - Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents
BT  - 12th International Conference on Document Analysis and Recognition
SP  - 1305
EP  - 1309
N2  - 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.
SN  - 1520-5363
L1  - http://refbase.cvc.uab.es/files/RRF2013.pdf
UR  - http://dx.doi.org/10.1109/ICDAR.2013.264
N1  - DAG
ID  - L. Rothacker2013
ER  -