Text-Graphics Separation to Detect Logo and Stamp from Color Document Images: A Spectral Approach

Text and graphics separation is an important task in the field of document image processing. This work aims at detecting graphics such as logos and stamps in a scanned document image. A novel spectral filtering based text-graphics separation algorithm (SFTGS) is presented here. The property of text that it is the major source of high spatial frequency components in a document image, is exploited in this algorithm. Accordingly high frequency filtering is used to separate the text symbols. This is followed by a segmentation process for delineating residual text and the graphics. The main advantage of SFTGS is that it works in a single pass, and can discriminate graphics and text without supervised training. Subsequently, the graphics segments are further categorized into two different classes, namely logos and stamps. In this case, we assume that these are the two classes of graphical objects present in the documents. The technique is evaluated using publicly available document dataset consisting of graphics as stamps and logos. The result is compared with existing approaches reported in the literature, and it is found that the proposed method performs superior to them. An overall performance of 89.1% recall and 96.9% precision is obtained for SFTGS.