Cloud of Line Distribution for Arbitrary Text Detection in Scene/Video/License Plate Images

Abstract

Detecting arbitrary oriented text in scene and license plate images is challenging due to multiple adverse factors caused by images of diversified applications. This paper proposes a novel idea of extracting Cloud of Line Distribution (COLD) for the text candidates given by Extremal regions (ER). The features extracted by COLD are fed to Random forest to label character components. The character components are grouped according to probability distribution of nearest neighbor components. This results in text line. The proposed method is demonstrated on standard database of natural scene images, namely ICDAR 2015, video images, namely ICDAR 2015 and license plate databases. Experimental results and comparative study show that the proposed method outperforms the existing methods in terms of invariant to rotations, scripts and applications.

Yirui Wu

Young Professor, CCF Senior Member

My research interests include Computer Vision, Artifical Intelligence, Multimedia Computing and Intelligent Water Conservancy.