手写体数字识别中的平移和旋转研究

Huan Wang, Y. Wan
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引用次数: 0

摘要

手写体数字识别是机器学习和神经网络中的经典问题。它在模式识别中起着重要的作用。在传统的BP神经网络中,在对测试图像进行平移或旋转时,识别准确率会明显下降。然而,当提取原始图像的特征作为神经网络最感兴趣的输入时,神经网络的性能得到了提高。本文提出了原始图像处理和特征提取两种新方法。一种方法是添加滑动窗口并将适当的单元转换为中心图像。另一种是旋转输入图像,使其轴线与其主要方向对齐。这是通过对输入图像进行主成分分析(PCA)并找出其主方向来实现的。实验结果表明,上述问题得到了解决,识别准确率从96.66%提高到97.12%。实践证明,该方法简单有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Translation and Rotation in Handwritten Digits Recognition
Handwritten digits recognition is a classical problem in machine learning and neural networks. It plays an important role in pattern recognition. In the traditional BP Neural Network, the recognition accuracy rate will drop significantly while translating or rotating test images. However, when extracting the features of the original images as the input in which the neural network is most interested, the performance of the network is improved. This paper proposes two new methods of processing the original images and extracting the feature. One is to add a sliding window and translate appropriate units to center images. The other is to rotate the input image to align its axes to its principle directions. This is achieved by Principal Component Analysis(PCA) to the input image and find its principle directions. According to the experiment results, the problem as above has been solved, and the recognition accuracy rate has improved from 96.66% to 97.12%. It is proved that the methods are simple but effective.
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