Adaptive Fuzzy Text Segmentation in Images with Complex Backgrounds Using Color and Texture

Abstract

Textual information present in images can help to achieve the aim of automatic content based annotation and retrieval of images. In this paper, we address the problem of text segmentation (TS) in images with complex background for recognition purposes. The proposed TS method takes as input the localized text and proceeds as follows: First, the number of initial clusters is determined by analyzing the colors of the image. Second, the image pixels are clustered using the number of clusters defined in the first step. The compactness of the clusters is evaluated in each step and improved iteratively to avoid possible oversegmentation of characters. Finally, an algorithm based on a rating scheme is proposed to determine the cluster where the text pixels are classified. The proposed method is evaluated on the basis of recognition results instead of visual segmentation results. Comparative experimental results using a test set of 2684 characters are reported.