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We propose a script independent bayesian framework for keyword spotting in multilingual handwritten documents. The approach relies on local character level score and global word level hypothesis scores and learns a bayesian logistic regression classifier to distinguish between keywords and non-keywords. In a bayesian
Keyword spotting (KWS) is an essential technique for speech information retrieval. When doing offline keyword query on large volume spontaneous speech data, fast and accurate KWS methods are required. In this paper, a novel phone-state matrix based vocabulary-independent KWS method is proposed, which has merits of
We propose the Bayesian Active Learning by Disagreement (BALD) model for keyword spotting in handwritten documents. In the context of keyword spotting in handwritten documents, the background text is all regions in the document that do not contain the keywords. The model tries to learn certain characteristics of the
Keyword spotting aims to retrieve all instances of a given keyword from a document in any language. In this paper, we propose a novel script independent line based word spotting framework for offline handwritten documents based on Hidden Markov Models. The methodology simulates the keywords in model space as a
We present a handwritten text Keyword Spotting (KWS) approach based on the combination of KWS methods using word-graphs (WGs) and character-lattices (CLs). It aims to solve the problem that WG-based models present for out of vocabulary (OOV) keywords: since there is no available information about them in the lexicon
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
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