Edge color transform: a new operator for natural scene text localization

Abstract

Text extraction is one of the most intuitive and natural ways in understanding of natural scene images. Localization is the first and one of the most important steps in this problem. In this paper, we propose a novel method for localizing text in natural scene images. We introduce a new operator so called Edge Color Transform (ECT) to solve this problem. After extracting the edge map of the input image, this operator follows the gradient direction (and the opposite direction) to assign the nearest color to each of the edge pixels. In the next step, each channel (Red, Green and Blue) of these color pixels are normalized to make them more robust to intensity changes. In the following step, these colored pixels are grouped together using a modified version of region growing algorithm. In this customized version of the algorithm, pixels of a region do not have to be spatially connected and their connectivity is decided based on a predefined oval-shaped neighborhood. When the regions are forged, each of them form a text candidate region. Subsequently, all of these candidates are applied to a classifier to extract text regions. Experimental results show that the proposed method performs well on both Farsi and well-known ICDAR 2013 data sets.