A simple text/graphic separation method for document image segmentation

Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper presents a method to separate the textual and non textual components in document images using a graph-based modeling and structural analysis. This is a fast and efficient method to separate adequately the graphical and the textual parts of a document. We have evaluated our method on two well-known subsets: the UW-III dataset and the ICDAR 2009 page segmentation competition dataset. Comparisons are led with two methods of state-of-the-art; these results showing that our method proved better performances in this task.