在线手写词识别的最小分类误差训练

Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition Pub Date : 2002-08-06 DOI:10.1109/IWFHR.2002.1030885

A. Biem

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引用次数: 6

摘要

本文描述了最小分类误差(MCE)训练准则在在线无约束风格词识别中的应用。所描述的系统使用allography - hmm来处理写入器的可变性。结果显示，在5k到10k的词汇表上，与基线最大似然系统相比，MCE训练实现了大约17%的单词错误率降低。

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Minimum classification error training for online handwritten word recognition

We describe an application of the minimum classification error (MCE) training criterion to online unconstrained-style word recognition. The described system uses allograph-HMMs to handle writer variability. The result, on vocabularies of 5k to 10k, shows that MCE training achieves around 17% word error rate reduction when compared to the baseline maximum likelihood system.

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Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition

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