在线手写识别的N-gram和N-class模型

Freddy Perraud, C. Viard-Gaudin, E. Morin, P. Lallican
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引用次数: 19

摘要

本文强调了语言模型在提高在线手写识别系统性能方面的作用。基于统计方法的模型,在书面语料库上训练,已经被调查。研究了两种模型:n-gram模型和n-class模型。在后一种情况下,类要么来自语法标准,要么来自上下文标准。为了将其集成到小容量系统(移动设备)中,结合这些标准设计了一个n级模型。它优于基于n-gram的笨重模型。集成到在线手写识别系统中,由于该语言模型,性能得到了实质性的改善。
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N-gram and N-class models for on line handwriting recognition
This paper highlights the interest of a language modelin increasing the performances of on-line handwritingrecognition systems. Models based on statisticalapproaches, trained on written corpora, have beeninvestigated. Two kinds of models have been studied: n-grammodels and n-class models. In the latter case, theclasses result either from a syntactic criteria or acontextual criteria. In order to integrate it into smallcapacity systems (mobile device), an n-class model hasbeen designed by combining these criteria. It outperformsbulkier models based on n-gram. Integration into an on-linehandwriting recognition system demonstrates asubstantial performance improvement due to the languagemodel.
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