Robust scene text detection using integrated feature discrimination
Qixiang Ye, David Doermann · 2014
Scene text detection in images of cluttered backgrounds and/or multilingual context is very challenging. In this paper, we propose a discriminative approach that integrates appearance and consensus features for robust scene text detection. We propose an integrated discrimination model to perform text classification as well as control component grouping. We design shape, stroke and structural features to describe text component appearance and the consensus among them. Experimental results on three public datasets show that the proposed approach is robust to cluttered backgrounds, and is applicable in multilingual environments.