Directional correlation analysis of local Haar binary pattern for text detection

Two main restrictions exist in state-of-the-art text detection algorithms: 1. Illumination variance; 2. Text-background contrast variance. This paper presents a robust text characterization approach based on local Haar binary pattern (LHBP) to address these problems. Based on LHBP, a coarse-to-fine detection framework is presented to precisely locate text lines in scene images. Firstly, threshold-restricted local binary pattern is extracted from high-frequency coefficients of pyramid Haar wavelet. It preserves and uniforms inconsistent text-background contrasts while filtering gradual illumination variations. Subsequently, we propose a directional correlation analysis (DCA) approach to filter non-directional LHBP regions for locating candidate text regions. Finally, using LHBP histogram, an SVM-based post-classification is presented to refine detection results. Experimental results on ICDAR 03 demonstrate the effectiveness and robustness of our proposed method.