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- W2045706564 abstract "The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach." @default.
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- W2045706564 date "2011-09-16" @default.
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- W2045706564 title "A keyword spotting approach using blurred shape model-based descriptors" @default.
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- W2045706564 doi "https://doi.org/10.1145/2037342.2037356" @default.
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