Handwritten Character Recognition Based on BP Neural Network

This paper researches on the issue of computer recognition to the handwritten character images, including lowercase letters and Arabic numerals. In this paper, we preprocess on characters in order to unified the basic features. And then, we apply the basic method of making the grids to extract the features of character, and classify the respectives. At last, we apply the latest heuristic modifications of Backpropagation algorithm to recognize the handwritten characters successfully. The basic datas of this research are testing and debugging on visual studio 2005, a large number of test experiments´ datas show that the discrimination of heuristic modifications of Backpropagation algorithm is up to 95 percentage, further improving validity and correctness of this latest algorithm.