Handwritten character recognition technology combined with artificial intelligence

Abstract

The offline handwritten character recognition is an important branch of character recognition, which involves character recognition, image processing, digital signal processing, artificial intelligence, fuzzy mathematics, information theory, computer and other disciplines. In the process of off-line handwritten character recognition, it only processed two-dimensional character dot images, and there are problems such as too many character classes, complex font structure, large deformation of handwritten characters and other issues. Currently off-line handwritten character recognition technology is still immature, which is still in the laboratory research stage. This paper attempted to combine neural network technology and character recognition technology, and came up with effectively new method of handwritten character recognition, finding a feasible new way to solve practical difficulties between handwritten character recognition. In this paper, according to the building process of the multi-level recognition system, the article introduced the preprocessing of character image, character feature extraction, neural networks and optimization algorithms as well as multiple neural networks in turn. In the end, based on experimental results by identification system in Matlab, the author comparatively discussed the performance of the recognition system, analyzing the notices of establishing the identification system.

Keywords:

• Handwritten character recognition
• Feature extraction
• Neural network training
• Multi-level neural network