Research on Nonlinear Distorted Image Recognition Based on Artificial Neural Network Algorithm

To study nonlinear distortion image recognition technology. Through the study of neural networks, an image recognition model based on BP neural network is proposed: An improved algorithm for driving quantity factor. According to the established neural network model, 10 commonly used images of Arabic numeral characters are recognized. The effectiveness of the model is verified by experiments with the extracted feature parameters of the target image. The results show that 38 of the 40 distorted images with noise can be correctly identified and 2 of them can be incorrectly identified by the single-stage recognition network, and the recognition rate reaches 95%; the recognition rate of cascade network reaches 100%. Therefore, the BP network which drives the number term can accelerate the training time of the network and improve the recognition efficiency of the system.

Automated summary

This study focuses on nonlinear distortion image recognition technology and proposes an image recognition model based on the BP neural network. Experimental results demonstrate the effectiveness of the model, indicating that the BP network driving the number term can improve the recognition efficiency of the system.