基于脊波变换的金融汉字不变性特征提取方法

为了满足对多方向选择性的要求，提出一种基于脊波变换的手写体金融汉字的不变性特征提取方法。该方法首先利用Radon变换将原始图像的旋转转换成Radon域的环形移位，再利用傅里叶变换振幅具有平移不变性的特点，在Radon域应用一维傅里叶变换，得到的振幅矩阵具有旋转不变性，它对旋转不变特征提取是非常理想的；然后沿振幅矩阵行的方向执行一维多分辨小波变换，使得从频域适当的子带提取特征成为可能；从Ridgelet子带中提取均值、标准差和能量组成特征向量。通过实验的验证，该方法可以满足表单自动处理系统应用对手写体金融汉字识别的要求。

Invariant feature extraction of amount Chinese characters based on Ridgelet transform

To meet the requirements of multi-directional choice, a new approach to the invariant feature extraction of handwritten amount Chinese characters was raised, with Ridgelet transform as its foundation. As far as this approach is concerned, first of all, the original images would be rotated to the Radon circular shift by means of Radon transform. On the basis of the characteristic that Fourier transform is row shift invariant, then, the one-dimensional Fourier transform would be adopted in Radon field to gain the conclusion that magnitude matrixes bear the rotation-invariance as a typical feature, which was pretty beneficial to the invariant feature extraction of rotation. When this was done, one-dimensional wavelet transform would be carried out in the direction of rows, thus achieving perfect choice of frequency, which made it possible to extract the features of sub-line in the appropriate frequencies. Finally, the average values, standard deviations and the energy values would form the feature vector which was extracted from the Ridgelet sub-bands. The approaches mentioned in the paper could satisfy the requirements from the form automatic processing on the recognition of handwritten amount Chinese characters.