A novel recognition method called Active Discriminant Function (ADF) for handwriting recognition is presented.

As the most simple and directive method of man-machine conversation, handwriting recognition technique played an important role in intelligent device.

Handwritten recognition of Chinese characters is one important branch of pattern recognition and AI, which turns out to be a great challenge to computer owing to large quantity and written flexibility.

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 algorithm on handwriting character recognition;

Small Set Handwritten Chinese Character Recognition Based on Integration RBF Neural Network;

By choosing stroke and substroke as basic components to analyse the composition regularities and deformation regularities of handwritten Chinese character, two models are presented for representing the structure for handwritten Chinese character recognition: the substroke center model and the stroke relation matrix model, as the classifying criteria and methods based on models.

According to the feature extraction of the handwritten Chinese character recognition, an improved extracting stroke plane method was proposed.