卷积深度神经网络的手写汉字识别系统

针对传统两级手写汉字识别系统中手写汉字识别的特征提取方法的限制问题，提出了一种采用卷积神经网对相似汉字自动学习有效特征进行识别的系统方法。该方法采用来自手写云平台上的大数据来训练模型，基于频度统计生成相似子集，进一步提高识别率。实验表明，相对于传统的基于梯度特征的支持向量机和最近邻分类器方法，该方法的识别率有一定的提高。

Handwritten Chinese character recognition system based on neural network convolution depth

For the feature extraction methods’restrictions problem of handwritten Chinese character recognition in traditional two hand written Chinese character recognition system, the paper proposes an identification system method which uses convolution neural network to automatically learn Chinese characters similar characteristics. The method uses data from large handwritten cloud platform to train the model, generating similar frequency statistics based on a subset of, and further improve the recognition rate. Experimental results show that the recognition rate with respect to the traditional gradient-based feature support vector machine and nearest neighbor classifier method, this method has improved to some extent.