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Vast collections of documents available in image format need to be indexed for information retrieval purposes. In this framework, word spotting is an alternative solution to optical character recognition (OCR), which is rather inefficient for recognizing text of degraded quality and unknown fonts usually appearing in printed text, or writing style variations in handwritten documents. Over the past...
In this paper, we address the problem of word spotting using a shape-based matching scheme between segmented word images represented by local contour features. As in a typical query-by-example (QBE) paradigm, a user selects an instance of the query word from the collection of interest and a ranked list of images is returned, based on their similarity with the query. This is accomplished in two steps...
In this paper, we propose a method for spotting keywords in images of handwritten text. Relying on an object detection system in real images, local contour features are extracted from segmented word images in order to obtain a representative shape of a word-class. Thus, word spotting is cast following a query-by-word-class scenario where class models are generated using a random subset of the images...
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