Robust text detection via multi-degree of sharpening and blurring

Abstract

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.