Handwritten Character Recognition Research Based On Quantum Neural Network

With the development of society, the ha n d wr i t t e n-character r e c o g n i t i o n technique has been widely applied to production and daily life. Although the research about the technique has went on 30 years, it is also focus on how to make the handwritten character recognition technique much more common and have higher recognition rate.The innovative ideas and the main work in this paper are as follows.At first, some contents of handwritten-character recognition are instructed, just as the research background, current state of research, research difficulty and application. And then, the principle and implementation of pretreatment processes of handwritten character swatch are presented in this paper. The processes include image noise reduction, binarization, thinning of image, normalizing method.Second, four common methods of feature extraction: rough grid feature extraction, stroke-density feature extraction, the traditional projection characteristic feature extraction and wavelet muhiresolution analysis feature extraction are also researched in this paper. On the basis of these researches, two new methods are introduced. The one is the blend features extraction which is composed of rough grid feature and stroke-density feature extraction, and the other is also a blend features extraction which combines the traditional projection characteristic and wavelet muhiresolution analysis.Third, based on the research of the basic principle and all kinds of learning algorithm of QNN, two new handwritten-character recognition methods are presented in this paper. The one is composed of QNN and the grid feature and stroke-density feature extraction, the other is composed of QNN and a blend features extraction, which combines the traditional projection characteristic and wavelet muhiresolution analysis feature extraction. The simulator results show that common and higher recognition rate for three swatch types include MNIST handwritten number, Chinese character and English letter. Also, two methods are compared and analysed in the paper. At last, it makes the summary and review of the whole paper and the forward research aim.