| 基于非线性PCA神经网络的手写体字符识别 |
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| 引用本文： | 孙光民, 张程, 王鹏, 邓超. 基于非线性PCA神经网络的手写体字符识别[J]. 北京工业大学学报, 2007, 33(9): 915-919. |
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| 作者姓名： | 孙光民 张程 王鹏 邓超 |
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| 作者单位： | 1.北京工业大学 电子信息与控制工程学院, 北京 100022 |
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| 摘 要： | 非线性主分量分析PCA算法与子空间模式识别方法相结合,提出了一种应用于手写体字符识别的基于非线性PCA神经网络的信号重构模型,并与BP网络模型进行了比较实验,结果表明,本文提出的方法,对于0~9手写体数字识别,正确识别率达到了94.74%,而对于a~z手写体字符识别,正确识别率达到了91.03%.
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| 关 键 词： | 文字识别 主分量分析 神经网络 |
| 文章编号： | 0254-0037（2007）09-0915-05 |
| 收稿时间： | 2006-06-29 |
| 修稿时间： | 2006-06-29 |
Handwritten Character Recognition Based on the Nonlinear PCA Neural Network |
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| SUN Guang-min, ZHANG Cheng, WANG Peng, DENG Chao. Handwritten Character Recognition Based on the Nonlinear PCA Neural Network[J]. Journal of Beijing University of Technology, 2007, 33(9): 915-919. |
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| Authors: | SUN Guang-min ZHANG Cheng WANG Peng DENG Chao |
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| Affiliation: | 1.College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China |
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| Abstract: | Principal component analysis(PCA)has been applied widely in pattern recognition.Based on the nonlinear PCA algorithm and subspace pattern recognition method,a nonlinear PCA neural network model of signal reconstruction has been proposed in this paper.The method has been used in handwritten digits and characters recognition,and a comparison with BP neural network based classifiers has been made.Some satis- factory results have been obtained.The experiment results show that the average correct identification rate of our method is up to 94.74% for the handwritten digits,and 91.03% for the handwritten characters. |
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| Keywords: | character recognition PCA neural networks |
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