Robust text detection via multi-degree of sharpening and blurring

Text detection is an important process for many content-based image analysis tasks. In this paper, we propose an approach to scene text detection via multi-degree of sharpening and blurring. Input image is sharpened and blurred with unsharp masking (USM) and bilateral filter. Then components are extracted with Maximally Stable Extremal Regions (MSER) from origin and processed images. Color, spatial layout and distance features of component are calculated, and features are weighted to construct the text candidates with distance function where the weights of features were trained before. At last, text candidates are estimated with a character classifier and the non-text candidates are eliminated. Experiments show that the proposed approach is robust to complex backgrounds and low image quality. We apply multi-degree sharpening and blurring before component extraction.Color, spatial layout and distance features are extracted to construct text candidates.Recall figure will increase if more preprocesses are applied.Multi-degree preprocesses perform well when dealing with complex backgrounds and low image quality.