Text Detection in Natural Scenes with Salient Region

In this paper, we present a novel approach to detect text in natural scenes. This approach is a type of bionic method, which imitates how human beings detect text exactly and robustly. Practically, human beings follow two steps to detect text: the first step is to find salient regions in a scene and the second step is to determine whether these salient regions are text or not. Therefore, two similar steps namely salient regions computation and text localization are used in our method. In the step of salient regions computation, a set of salient features including multi-sacle contrast, modified center-surround histogram, color spatial distribution and similarity of stroke width are used to describe an image, following with computation of salient regions based on the combination of Conditional Random Fields model and above features. Because sole letter rarely appear, in the step of text localization, salient regions are segmented and the connected components are grouped into text strings based on their features such as spatial relationships, color difference and stroke width. As an elementary unit, the text string is refined by connected component analysis. We tested the effectiveness of our method on the ICDAR 2003 database. The experimental results show that the proposed method provides promising performance in comparison with existing methods.