Scene Text Localization Using Gradient Local Correlation

In this paper, we propose an efficient scene text localization method using gradient local correlation, which can characterize the density of pair wise edges and stroke width consistency to get a text confidence map. Gradient local correlation is insensitive to the gradient direction and robust to noise, small character size and shadow. Based on the text confidence map, the regions with high confidence are segmented into connected components (CCs), which are classified to text CCs and non-text CCs using an SVM classifier. Then, the text CCs with similar color and stroke width are grouped into text lines, which are in turn partitioned into words. Experimental results on the ICDAR 2003 text locating competition dataset demonstrate the effectiveness of our method.