基于深度学习的异噪声下手写汉字识别的研究.

The problem that the recognition rate of handwritten Chinese characters is affected by random noise, this paper proposed a new algorithm based on deep learning and noise suppression. This algorithm was mainly aimed at handwritten Chinese character characters and pictures with random noise. It was a model that used Caffe platform to establish noise suppression and convolutional neural networks in the Python environment. It removed noise and correctly recognized handwritten Chinese characters. In addition, the new algorithm did not change the character while removing noise, and retained the original Chinese character information. As a result, the noise intensity of two different types of noise (Gaussian noise and salt-and-pepper noise) was gradually increased, it performed multiple experiments and compared them with other methods, the average recognition rate was 97. 05%. The experimental results show that the model and algorithm have the advantages of high efficiency and strong recognition ability. [ABSTRACT FROM AUTHOR]