基于深度学习的异噪声下手写汉字识别的研究

针对手写汉字字符图像识别率受随机噪声影响的问题，提出了一种基于深度学习与抑制噪声相结合的新算法。该算法主要应用于拥有随机噪声的手写汉字字符图片，是其在Python环境下，利用Caffe平台建立抑制噪声与卷积神经网络相结合的模型，通过模型移除噪声并正确识别手写汉字。另外，新算法去除噪声的同时对字符形态没有改变，保留了汉字的原始信息。结果在其两种不同的噪声（高斯噪声和椒盐噪声）下，逐渐提升其噪声强度，进行多次实验，同时与其他方法对比，最终得到其平均识别率为97.05%。实验结果表明，该模型和算法具有效率快、识别能力强的优点。

Research on handwritten Chinese character recognition based on deep learning with different noise

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.