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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
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
paper, we propose a Bayesian approach to region-based image annotation, which integrates the content-based search and context into a unified framework. The content-based search selects representative keywords by matching an unlabeled image with the labeled ones followed by a weighted keyword ranking, which are in turn used
To perform a semantic search on a large dataset of images, we need to be able to transform the visual content of images (colors, textures, shapes) into semantic information. This transformation, called image annotation, assigns a caption or keywords to the visual content in a digital image. In this paper we try to
. The color histograms, Texture, GIST and invariant moments, used as features extraction methods, are combined together with multiclass support vector machine, Bayesian networks, Neural networks and nearest neighbour classifiers, in order to annotate the image content with the appropriate keywords. The accuracy of the
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