基于Wiener滤波,K-L变换和BP网络的数字识别

研究基于传统模板匹配的识别灰度图像中数字的方法.在对大量样本图像模板进行Wiener滤波的基础上,利用K-L变换进行特征提取,用低维子空间描述高维空间中的图像.将低维子空间中的向量加载到BP网络的输入端进行训练,调整神经网络权值.权值稳定后,在网络输入端加载经过预处理的待识别灰度数字图像,在输出端即可得到识别结果.该方法有效地利用了Wiener滤波器以最小方差对原始信号的恢复能力、K-L变换的降维特性和BP学习网络所擅长的数据映射处理能力.

Digit Character Recognition Based on Wiener Filter, Karhunen-Loeve Transform and BP Network

A method to recognize digit characters in intensity images is provided based on traditional way of template matching. After operation of Wiener filtering on a lot of sample image templates, Karhunen Loeve transform has been used to extract features and describe the high dimensional images with low dimensional matrices. Then these vectors in the low dimensional space were loaded onto the input layer of BP network and started training. Weights were adjusted until a stable status was reached, and when preprocessing intensity images to be recognized were loaded onto the input layer, recognition results were obtained at the output layer. The Wiener filter has a good performance in recovering original signal with minimum mean square error, K L transform can reduce the dimensionality of eigenspace and BP network does well in data mapping.