基于压缩感知的阅卷系统手写汉字识别算法

针对阅卷系统中手写汉字识别率和识别精度低的问题，文中提出一种基于压缩感知理论的阅卷系统手写汉字识别算法。该算法首先对阅卷系统手写汉字图像进行随机采样得到其特征；然后对其进行稀疏表示，并最小化其l1范数以得到样本的稀疏解；最后利用该稀疏解的系数判别测试样本的类别。该方法用对信号的随机采样替代了传统的特征提取方法，简化了算法的实现过程，同时用现有的训练样本组成训练字典，避免了复杂的训练过程。该算法在手写汉字数据库ETL9B上的识别率达到99.1%。

Handwritten Chinese Character Recognition Algorithm Based on Compressed Sensing

In view of the low recognition rate and recognition accuracy of handwritten Chinese character in marking system, a handwritten Chinese character recognition algorithm based on compressed sensing theory was proposed. The algorithm first randomly sampled the handwritten Chinese character in the marking system, and got its features. Then, it made sparse representation and minimized its l1 norm to get the sparse solution of the sample. Finally, the obtained sparse coefficient was used to identify the categories of the test samples. This method replaced the traditional method of feature extraction with random sampling of signals, which simplified the realization process of the algorithm. At the same time, with the usage of training dictionary formed by the existing training samples, the complicated training process was avoided. Study results showed that the recognition rate of the algorithm in handwritten Chinese character database ETL9B reached 99.1%.