Character Recognition System Design Based on Image Feature Extraction and QNN

In order to improve the accuracy and reliability of character recognition, combining the image processing technology with the quantum neural network (QNN), the character recognition system based on QNN is studied. Coarse mesh feature method is used to extract the image features. At the same time, in order to enhance the ability of coarse mesh method to resist the change of position, the character image is located and translated to the center of the template before the feature extraction. Then, the quantum neural network based on multilayer excitation function is used to recognize characters. The simulation experiments using MATLAB show that the quantum neural network has better recognition efficiency, and even the accuracy rate can reach more than 90%, and strong noise immunity, and better classification. This shows that the system can indeed improve the correct rate of recognition to a certain extent and achieved the desired results.