一种云平台下高识别率的手写汉字光学图像识别系统

针对原有手写汉字识别系统中文字特征提取的相关问题，笔者在本篇文章中设计了一类全新的方法，本方法结合卷积神经网对字形相似的字智能化学习有效特征，并且采用了光学图像识别技术，另外，这类方法通过手写云平台中丰富的数据资源对模型进行高效的训练，同时根据频度统计形成特定的相似子集，以此有效的优化识别率。实验结果显示，和原有的支持向量机(SVM)以及最近邻分类器方法进行系统性的对比，本文所论述的方法能够显著提升识别率。

An optical image recognition system for handwritten Chinese characters with high recognition rate in cloud platform

For the feature extraction methods’ restrictions problem of handwritten chinese character recognition in traditional two handwritten cinese character recognition system, the paper proposes a 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 (SV and nearest neighbor classifier method, this method has improved to some extent.