Online Recognition of Unconstrained Handwritten Japanese Text Using Statistical Information

Abstract : We developed an online recognition system to read unconstrained handwritten Japanese texts using statistical information. The substantial problems of reading handwritten Japanese text are how to correctly segment freely written characters and how to correct errors of character classification. Our method searches for the best interpretation by integrating the likelihoods of character segmentation, character classification and language processing. For the language processing, candidates of words in any position are extracted from a dictionary including about 240,000 words, and the extracted words are evaluated using their grammatical connective probability and word bi-gram probability as the context. Experiments using 467 texts showed that our complete method is more accurate than any partial method. The rate of recognition per text improved from 10 to 38%, and the rate of recognition per character improved from 72.2 to 82.1%. The effectiveness of our method was proved.
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Communication dans un congrès
Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006
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https://hal.inria.fr/inria-00103732
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Soumis le : jeudi 5 octobre 2006 - 11:10:22
Dernière modification le : jeudi 5 octobre 2006 - 11:19:53
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  • HAL Id : inria-00103732, version 1

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Katsumi Marukawa, Takeshi Nagasaki, Kazuyoshi Kikuta. Online Recognition of Unconstrained Handwritten Japanese Text Using Statistical Information. Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006. 〈inria-00103732〉

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