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• W2016197087 abstract "A context-free grammar-based language model for string recognition has been developed. The developed language model is implemented as a set of graphs which are equivalent to a recursive transition networks. This graph-structured model is not only more compact in memory size than the trie-structured model but also more powerful in describing variations of recognition-target phrases, such as use of characters, selection of words, and structures of phrases. This can improve recognition accuracy. We also developed a method to semi-automatically generate the language model from a collection of sample phrase texts and predefined variation rules. An algorithm to match this language model to the character-recognition results was devised. The model and the generation method have been successfully applied to practical applications such as address recognition. And experimental tests with freely-handwritten addresses show the effectiveness for implementation of the language model and the improvement of the recognition accuracy." @default.
• W2016197087 created "2016-06-24" @default.
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• W2016197087 date "2002-06-01" @default.
• W2016197087 modified "2023-09-27" @default.
• W2016197087 title "A Context-free Grammar-based Language Model for String Recognition" @default.
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• W2016197087 doi "https://doi.org/10.1142/s0219427902000613" @default.
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