To counter the complexity and limitation of traditional digital recognition methods,a kind of handwritten numeral recognition method based on BP neural network is proposed.

In this paper, many preprocessing ways of handwritten numeral are used.

Artificial Neural Network model for handwritten digit recognition based on template matching;

Handwritten Digit Recognition Method Based on Combination Features;

This paper presents the theory to recognize handwritten digits by the BP neural network,and an improved algorithm to accelerate the running speed of the network based on the analysis of the traditional BP algorithm s running speed.

In this paper we give a kind of handwritten digits recognition method based on BP neural network.

This paper studies handwritten numeral recognition.

Handwritten numeral recognition belongs to the field of pattern recognition, which is a hot field for a large number of researchers and also is a critical step in entry of information.

The handwritten digital recognition was implemented with the advantages of the powerful interface programming capability in VC++.

The handwritten digital recognition is an important problem of pattern recognition field.

To promote the research and application of handwritten digital recognition technology, the superiority of statistical unrelated optimal discriminant transform in handwritten digit recognition was presented and certified.

A new feature extraction method on handwritten digits recognition system;

A three-stage classification system of handwritten digits recognition is presented for the automatic analysis system in UK psychology test.

In this article, kernel functions are formally discussed in mathematical terms and two of the kernel based learning algorithms: Support Vector Machine (SVM) and Kernel Fisher Discriminant Analysis (KFD) are introduced and applied to handwritten digits recognition.