Abstract: Gaussian descriptors are contourbased shape features. They are invariant to translation, scaling changes, rotation and reflection. Compared to the existing shape features, they are more robust against noise and slight edge variations, and have lower computation complexity and higher recognition/retrieval rate. In addition, they are applicationindependent. In this paper, Gaussian descriptors were used as features for character recognition. A comparison with another contourbased moment invariants, which is an improvement and extension of classical Hu moments, was also given. Numerical experimental results show that Gaussian descriptors are an attractive tool for character recognition.

Key words: character recognition, Gaussian descriptors, Hu's moments, contour-based moment invariants

摘要： 高斯描绘子是一种基于边缘的形状特征，具有识别/匹配率高、相对于平移、旋转、尺度和反射不变、计算量小、对适度的边缘变动和噪声不敏感以及适用范围广等优点。将高斯描绘子用于字符识别，并与另一种基于边缘的特征轮廓矩不变量Hu矩的推广和改进进行比较。实验结果表明，高斯描绘子有很好的识别效果。

关键词: 字符识别, 高斯描绘子, Hu矩, 轮廓矩不变量