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.