Robust scene text detection for multi-script languages using deep learning

Abstract

Text detection in natural images has been a high demand for a lot real-life applications such as image retrieval and self-navigation. This work deals with the problem of robust text detection especially for multi-script in natural scene images. Unlike the existing works that consider multi-script characters as groups of text fragments, we consider them as non-connected components. Specifically, we firstly propose a novel representation named Linked Extremal Regions (LER) to extract full characters instead of fragments of scene characters. Secondly, we propose a two-stage convolution neural net- works for discriminating multi-script texts in clutter background images for more robust text detection. Experimental results on three well-known datasets, namely, ICDAR 2011, 2013 and MSRA-TD500, demonstrate that the proposed method outperforms the state-of-the-art methods, and is also language independent.