Text detection in scene images based on exhaustive segmentation

In recent years, significant progress has been made in detecting text in scene images. However, most of state-of-the-art approaches can not work well when encountered blurred, low-resolution and small-sized texts. We consider many connected regions as candidates, which aim to capture character regions as many as possible. In this paper, we propose a novel method, which is based on exhaustive segmentation, to detect text in scene images. Firstly, we present a parallel structure to generate character candidate regions with the exhaustive segmentation of scene image. Secondly, a well-designed two-layer filtering method is used to filter out non-character candidate regions. Finally, at text line grouping stage, the edges of the fully connected graph of the remaining character candidate regions are cut by a support vector machine classifier. We use two public datasets, namely, ICDAR 2013 dataset and the Street View Text dataset to evaluate the performance of our method. Experimental results show that our method achieves excellent recall rate on these two public datasets, moreover, our method is robust to the blurred, low-resolution and small-sized texts.