A hybrid large vocabulary handwritten word recognition system using neural networks with hidden Markov models

被引：19
|
作者：
Koerich, AL [1 ]
Leydier, Y [1 ]
Sabourin, R [1 ]
Suen, CY [1 ]
机构：
[1] Ecole Technol Super, Lab Imagerie Vis & Intelligence Artificielle, Montreal, PQ H3C 1K3, Canada
来源：
EIGHTH INTERNATIONAL WORKSHOP ON FRONTIERS IN HANDWRITING RECOGNITION: PROCEEDINGS | 2002年
关键词：
D O I：
10.1109/IWFHR.2002.1030893
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we present a hybrid recognition system that integrates hidden Markov models (HMM) with neural networks (NN) in a probabilistic framework. The input data is processed first by a lexicon-driven word recognizer based on HMMs to generate a list of the candidate N-best-scoring word hypotheses as well as the segmentation of such word hypotheses into characters. An NN classifier is used to generate a score for each segmented character and in the end, the scores from the HMM and the NN classifiers are combined to optimize performance. Experimental results show that for an 80,000-word vocabulary, the hybrid HMM/NN system improves by about 10% the word recognition rate over the HMM system alone.
引用
收藏
页码：99 / 104
页数：4
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