Offline grammar-based recognition of handwritten sentences.

Matthias Zimmermann,Jean-Cédric Chappelier,Horst Bunke

IEEE Transactions on Pattern Analysis and Machine Intelligence（2006）

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摘要

This paper proposes a sequential coupling of a Hidden Markov Model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using Stochastic Context-Free Grammars (SCFG) extracted from a text corpus. Based on extensive experiments, we conclude that syntax analysis helps to improve recognition rates significantly.

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关键词

handwritten sentences,text corpus,syntax analysis,probabilistic bottom-up chart parser,extensive experiment,stochastic context-free grammars,hidden markov model,offline handwritten english sentence,offline grammar-based recognition,recognition rate,sequential coupling,hidden markov models,artificial intelligence,context free grammars,speech recognition,handwriting recognition,stochastic processes,bottom up,algorithms,optical character recognition,text analysis,natural languages,semantics,stochastic context free grammar

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