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For text-query-based keyword spotting from handwritten Chinese documents, the index is usually organized as a candidate lattice to overcome the ambiguity of character segmentation. Each edge in the lattice denotes a candidate character associated with a candidate class. Character similarity (between character and class) scores are calculated on each edge, and the similarity between a query word and...
In keyword spotting from handwritten documents, the word similarity is usually computed by combining character similarities. Converting similarity to probabilistic confidence is beneficial for context fusion and threshold selection. In this paper, we propose to directly estimate the posterior probability of candidate characters based on the N-best paths from the segmentation-recognitioin candidate...
This paper proposes a lattice-based method for keyword spotting in online Chinese handwriting to improve the trade-off between accuracy and speed, and to overcome the out-of-vocabulary (OOV) problem of lexicon-driven approach. Using a character string recognition algorithm, the lattice-based method generates a candidate lattice of N-best list. We observe that search multiple candidate strings reduces...
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