PT Unknown
AU L. Rothacker
   Marçal Rusiñol
   G.A. Fink
TI Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents
BT 12th International Conference on Document Analysis and Recognition
PY 2013
BP 1305
EP 1309
DI 10.1109/ICDAR.2013.264
AB 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.
ER