针对原有手写汉字识别系统中文字特征提取的相关问题,结合 卷积神经网智能化学习字形相似字的 有效特征,设计了一种全新的手写汉字光学图像识别系统。通过手写云平台中丰富的数据资源对模型进行 高效训练,根据频度统计形成特定的相似子集,有效优化识别率。实验结果表明,与支持向量机(SVM)及 最近邻分类器方法相比,提出的方法能够显著提升识别率。

Aiming at the related problems of character feature extraction in the original handwritten Chinese characters recognition system, effective features of similar words are learned combining with convolution neural network intelligent, and a new optical image recognition system for handwritten Chinese characters is designed. Efficient training to models is carried out with the rich data resources in handwritten cloud platform. The specific similar subsets are formed according to frequency statistics, and the recognition rates are optimized effectively. Experimental results show that the proposed method can significantly improve recognition rate compared with the support vector machine (SVM) and nearest neighbor classifier method.