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Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template
extraction methods; multiclass support vector machine, multilayer neural network and nearest neighbour classifiers are combined together in order to classify and to find the appropriate keywords for this content. The color histograms and moments are used in this paper as features to represent image content. We support our case
semantic analysis (LSA) is employed to the NN based annotation scheme (noted as LSA-NN) for discovering the latent contextual correlation among the keywords, which is neglected by many previous annotation methods. Instead of region-level as most previous works do, the LSA-NN based annotation scheme is built at image-level to
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