The Analysis And Study Of Handwritten Digit Recognition Based On BP Neural Network

The neural networks is a collateral, nonlinear and redundancy system, and it makes the express, memory and treat of information varied from routine. It's the non-linearity that makes us could express the model that can't be expressed clearly by compute theory at least now; and it's the ability of self-study and self-organization that makes us can express those clearly by acting with exterior world, which can't be expressed by compute theory. The neural networks has unique predominance for system that can't make accurate mathematics model.Handwritten numeral recognition is a hotspot of study for years, and is some especial issue of character recognition. Handwritten numeral recognition is applied broadly in given environment. When come down to numeral recognition, the emphases people think is its dependability, especially refer to money-digit recognition. So one of the key steps for these questions is designing a high-dependability and high-accuracy handwritten numeral recognition system. But there are no system can achieve so good recognition effect.In the past years, researchers had put forward many recognition ways, which can be carved up to structure character oriented ways and statistic character oriented ways.Handwritten numeral recognition belongs to pattern recognition, and is a factual question of pattern recognition, so it has some special request. The handwritten numeral recognition system must be efficient and fast besides dependable and accuracy.It's difficult to make accurate mathematics model for handwritten numeral recognition, so BP neural networks is used here. The key steps of neural networks pattern recognition are preprocessing and feature subset selection. In this paper, algorithm of feature subset selection basing on the edge-outlineof characters has been adopted in handwritten numeral recognition, and the process of feature subset selection had been realized in program. Neural networks for handwritten numeral recognition had been trained in this paper, and its structure is two classes.Recognition system in this paper has achieved a good rate of recognition in random handwritten numeral by test.