A Neural Networks Multi-classifers Integrated Scheme Based on OCON for Handwritten Chinese Character Recognition

Abstract

Abstract: This paper proposes a scheme for handwritten Chinese character recognition (HCCR), which is made up of pre-classifiers and model integrated, and both use neural networks. Experiments show that the proposed scheme can classify large number of catalogs well. For example, testing 1 000×10 characters, the maximal recognition rate is 95.22%, 5.0%-8.7% higher than that of advisual classifer.

摘要： 提出了一种基于单字单网的手写体汉字识别纯神经网络的多分类器集成方案,并通过实验证明用该方案实现的神经网络集成系统性能均比任一个神经网络单分类器都好,对1 000种不同的手写体汉字的1 000×10个字进行测试,集成后的识别率最高达到95.22%,比单分类器的识别率高出5.0%-8.7%。

关键词: 手写体汉字识别, 多分类器集成, 神经网络

CLC Number:

TP183

WAN Hongmei. A Neural Networks Multi-classifers Integrated Scheme Based on OCON for Handwritten Chinese Character Recognition[J]. , 2004, 30(16): 161-162.

万红梅. 一种手写体汉字识别的神经网络多分类器集成方案[J]. , 2004, 30(16): 161-162.

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URL: https://www.ecice06.com/EN/Y2004/V30/I16/161

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