基于小波变换和DCT的字符图像特征抽取新方法

New Method of Feature Extraction Using Wavelet Transform and DCT in OCR

从特征矢量的不变性和抗噪性角度,提出了一种基于小波变换(WT)和离散余弦变换(DCT)的字符特征抽取新方法。利用圆周投影算法,把二维的字符图像转换为一维投影。基于WT和DCT的非线性变换,克服了因变形和噪声引起的一维投影的非线性变形失真。通过对识别不同大小、方向及噪声的字符仿真实验和不同特征抽取方法的对比实验,以及对工业标牌字符的识别,表明该特征抽取方法具有尺度和旋转不变性,有较好的抗噪声能力和很好的分类性能。

Considering the invariant nad the noise sensitivity of feature vectors,a new method based on the wavelet transform and the discrete cosine transform(DCT) was presented to extract features in character recognition.The circular projection algorithm was used to transform a character image with two independent variables into a function of one independent variable in the circular projection space.This representation of pattern in the circular projection space will be distorted nonlinearly in the presence of noise and variety.To solve the nonlinear distortion problem,a measure distance based on the wavelet and the DCT was proposed as a nonlinear metric.The derived one-dimensional pattern was decomposed a set of wavelet transformation subpatterns with Daubechies′ wavelet transformation.The feature vectors for the original two-dimensional pattern were readily computed to use the DCT.As an application,the tag pressed and printed characters were recognized by this method.The reslts show that the proposed feature vectors can yield an excellent classification rate on the condition of noise and deformity.So,this method is proved to be in scale and orientation invariant,insensitive noise.