基于小波变换和SVM的文本区域定位

提出了一种基于小波变换和支持向量机(SVM)在数字图像中定位文本的方法.对图像进行小波变换,并在低频概貌和高频能量空间应用SVM提取文本的纹理特征,由SVM来决定当前的像素是文本类还是非文本类.因为SVM的分类结果可能存在噪声或错误,用形态学去噪和计算纹理能量的方法对SVM的分类结果进行后处理.小波变换和SVM的结合,不仅降低了输入空间样本的数量,而且利用了SVM适合于高维空间工作的特点,提高了文本提取的效率.实验结果表明,提出的方法可以快速有效地定位数字图像中的文本区域.

Text Region Localization Using Wavelet Transform in Combination with Support Vector Machine

A method based on wavelet transform in combination with support vector machine(SVM) is proposed for localizing text region.After the image was decomposed by wavelet transform,SVM is used to extract the texture characteristics of text from the low-frequency vague image sub-space and high-frequency energy sub-space and determine what the current pixel belongs to,i.e.,the text class or nontext class.Then,because there are possible noise and false text after SVM classification,the methods of morphological denoising and textural energy calculating are used to reprocess the results of SVM classification.The proposed method utilizes the characteristic that SVM suits high-dimension space so as to improve the efficiency of extracting text in addition to reducing the number of spatial sample inputs.Experimental results showed that the method can rapidly and effectively localize the text region in digital image.