A Hybrid Approach to Detect Texts in Natural Scenes by Integration of a Connected-Component Method and a Sliding-Window Method

Text detection in images of natural scenes is important for scene understanding, content-based image analysis, assistive navigation and automatic geocoding. Achieving such text detection is challenging due to complex backgrounds, non-uniform illumination, and variations in text font, size, and orientation. In this paper, we present a novel hybrid approach for detecting text robustly in natural scenes. We connect two text-detection methods in parallel structure: (1) a connected-component method and (2) a sliding-window method and outputs basically both results. The connected-component method generates text lines based on local relations of connected components. The sliding-window method consisting of a novel Hough Transform-based method generates text lines based on global structure. These two text-detection methods can output complementary results, which enables the system to detect various texts in natural scenes. Testing with the ICDAR2013 text localization dataset shows that the proposed scheme outperforms the latest published algorithms and the parallel structure consisting of the two different methods contributes to decreasing false negatives and improves recall rate.