Abstract
This paper presents a novel variance based image binarization scheme for automatic segmentation of text from low resolution images. First, the variance based binarization scheme is separately carried out on the three color planes of the image. Then, we merge these planes to obtain final binarized image. This creates several connected components (CCs). Now, these CCs are studied in order to segment possible text CCs. Now, a number of features that classify between text and non-text components, are considered. Further, KNN and SVM classifiers are applied for the present two class classification problem. For the training of KNN and SVM, ground-truth information of text CCs and our laboratory made non-text CCs are considered. We conduct extensive experiments on publicly available ICDAR 2011 Born Digital Data set. Concerning comparison, we consider a number of previously reported methods. Our binarization scheme significantly outperforms the existing methods and segmentation results are also satisfactory.
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