Text detection in natural scene images based on color prior guided MSER

Abstract

Abstract In this paper, we focus on text detection in natural scene images which is conducive to content-based wild image analysis and understanding. This task is still an open problem and usually includes two key issues: text candidate extraction and verification. For text candidate extraction, we introduce a color prior to guide the character candidate extraction by Maximally Stable Extremal Region (MSER). The principle of color prior acquirement is to obtain stroke-like textures with modified Stroke Width Transform (SWT), which is based on segmented edges. For text verification, the ideology of deep learning is adopted to distinguish text/non-text candidates. To improve classification accuracy, the results of specific task CNNs are fused. The proposed framework is evaluated on the ICDAR 2013 Robust Reading Competition database. It achieves F-score at 85.87%, which are superior over several state-of-the-art text detection methods.