Text detection in images based on Multiple Kernel Learning

Detecting text accurately is an essential requirement for text recognition. In this paper, we propose a method to automatically detect text information in images. We firstly find the candidates of text regions based on the analysis of connected components and extract textural features in these candidate regions. We apply Multiple Kernel Learning to train a classifier with an optimal combination of kernels. The classifier can be used to distinguish text from icons which might be included in region candidates. Our method has been successfully implemented in detecting text from the interface images of mobile phones. According to the experimental results, our method outperforms several typical SVM based methods.