TU Dortmund Department of Computer Science LS XII Pattern Recognition GroupPublications → Publication Details

Bag-of-Features HMMs for Segmentation-Free Word Spotting in Handwritten Documents

Leonard Rothacker, Marcal Rusinol and Gernot A. Fink
Proc. Int. Conf. on Document Analysis and Recognition, Washington DC, USA, 2013.

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.

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