模糊逻辑和谱聚类的字符图像分割

Spectral clustering based text image segmentation using fuzzy logic

摘要:

为了从复杂背景中有效分离出字符图像,提出了一种基于模糊逻辑的谱聚类字符图像分割方法.利用最大信息熵准则获得模糊函数的参数,将原始图像模糊化;在模糊后的图像上建立像素间的相似矩阵,文本图像的纹理、灰度及像素间的距离是定义相似函数的依据,计算相似矩阵最小特征值对应的特征向量,并对其聚类划分;利用分类后的特征向量对相似矩阵进行划分,进而实现原图像的分割.实验结果表明:本文方法优于一般的阈值化分割方法,能够有效处理背景复杂的自然场景文本图像.

Abstract:

A novel approach of text images segmentation based on fuzzy logic and spectral clustering is proposed in this paper in order to extract the text from complex backgrounds.Parameters of the function are obtained according to the maximum entropy principle and the original image is fuzzified.Then gray,distance and textural information among pixels are extracted from the fuzzified image to construct the affinity matrix.The original image is segmented using the clustered eigenvector corresponding to the minimum eigenvalue of the matrix.Experimental results show that the proposed method is superior to the usual thresholding methods and can handle the natural scene text image with complex backgrounds.