Text detection and restoration in natural scene images

Abstract

A new method for text detection and recognition in natural scene images is presented in this paper. In the detection process, color, texture, and OCR statistic features are combined in a coarse-to-fine framework to discriminate texts from non-text patterns. In this approach, color feature is used to group text pixels into candidate text lines. Texture feature is used to capture the “dense intensity variance” property of text pattern. Statistic features from OCR (Optical Character Reader) results are employed to further reduce detection false alarms empirically. After the detection process, a restoration process is used. This process is based on plane-to-plane homography. It is carried out to refine the background plane of text when an affine transformation is detected on a located text and independent of camera parameters. Experimental results tested from a large dataset have demonstrated that the proposed method is effective and practical.