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The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to apply this framework to handwritten word-spotting. Given a word image and a keyword generative model, the idea is to generate a vector which
The so-called filler or garbage Hidden Markov Models (HMM) are among the most widely used models for lexicon-free, query by string key word spotting in the fields of speech recognition and (lately) handwritten text recognition. An important drawback of this approach is the large computational cost of the keyword
by supervised learning. Experiments on TUAT Kuchibue database show that the proposed method can effectively improve the system performance. When the search method with the optimal threshold retrieves for a keyword consisting of two, three or four characters, its f-measure is 0.720, 0.868 or 0.923, respectively.
In this paper, we propose a novel system for word spotting and regular expression detection in Handwritten documents. The proposed approach is lexicon-free, i.e., able to spot arbitrary keywords that are not required to be known at the training stage. Furthermore, the proposed system is segmentation-free, i.e., text
In this paper, we introduce an alpha-numerical sequences extraction system (keywords, numerical fields or alpha-numerical sequences) in unconstrained handwritten documents. Contrary to most of the approaches presented in the literature, our system relies on a global handwriting line model describing two kinds of
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