Zimmermann Matthias, Chappelier Jean-Cédric, Bunke Horst
International Computer Science Institute, Berkeley, CA 94704-1198, USA.
IEEE Trans Pattern Anal Mach Intell. 2006 May;28(5):818-21. doi: 10.1109/TPAMI.2006.103.
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.
本文提出了一种用于离线手写英语句子的隐马尔可夫模型（HMM）识别器与概率自底向上句法分析器的顺序耦合方法，该句法分析器使用从文本语料库中提取的随机上下文无关文法（SCFG）。基于大量实验，我们得出结论，句法分析有助于显著提高识别率。
