Skew correction and line extraction in binarized printed text images

Skew correction and text line extraction are essential steps for optical character recognition (OCR) applications. For this purpose, numerous approaches were developed, which conduct the analysis primarily in document images. However, they often suffer from limited detection range and application-specific parameter tuning. Inspired by the intrinsic properties of printed text, a novel subregion-based approach is proposed in this paper, which is applicable for generic printed text images and no parameter tuning is required. Guided by the spacing between text lines, the detection of a skew angle between ±90° is feasible. As verified by the experimental results, the proposed approach is robust to diverse skew directions and significantly improves the state-of-the-art OCR performance.