Text detection in natural scene with edge analysis

Text plays an important role in daily life due to its rich information, thus automatic text detection in natural scenes has many attractive applications. However, detecting such text is a challenge problem, because of the variations of scale, font, color, lighting and shadow. In this paper, we propose a method that detects text in natural scene through two steps of edge analysis, namely candidate edge combination and edge classification. In the step of candidate edge combination, the edge of input image is divided into small segments firstly. Then neighbor edge segments are merged, when they have similar stroke width and color. Through this step, each character is described by one edge segment set. Because sole letter rarely appears in natural scene, in the step of edge classification, candidate edges are aggregated into text chain, following with chain classification based on character-based and chain-based features. In order to evaluate the effectiveness of our method, we run our algorithm on the ICDAR 2011 public database and Street View Text database. The experimental results show that the proposed method provides promising performance in comparison with existing methods.