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by Ro L. Koerich, Robert Sabourin, Ching Y. Suen
In Proc. 4th International Workshop on Document Analysis Systems
http://www.cenparmi.concordia.ca/publications/pdf/2000/das010p.pdf
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Abstract:
Abstract. This paper describes a large vocabulary handwritten word recognition system based on a syntax{directed level building algorithm (SDLBA) that incorporates contextual information. The sequences of observations extracted from the input images are matched against the entries of a tree{structure lexicon where each node is represented by a 10{state character HMM. The search proceeds breadth | rst and each node is decoded by the SDLBA. Contextual information about writing styles and case transitions is injected between the levels of the SDLBA. An implementation of the SDLBA together with a 36,100{entry lexicon is described. In terms of recognition speed, the results show that the SDLBA together with the tree{structured lexicon outperforms a baseline system that uses a Viterbi { at{lexicon scheme while maintaining the same accuracy and consuming a reasonable amount of memory. 1
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