Object Reading: Text Recognition for Object Recognition

Abstract

We propose to use text recognition to aid in visual object class recognition. To this end we first propose a new algorithm for text detection in natural images. The proposed text detection is based on saliency cues and a context fusion step. The algorithm does not need any parameter tuning and can deal with varying imaging conditions. We evaluate three different tasks: 1. Scene text recognition, where we increase the state-of-the-art by 0.17 on the ICDAR 2003 dataset. 2. Saliency based object recognition, where we outperform other state-of-the-art saliency methods for object recognition on the PASCAL VOC 2011 dataset. 3. Object recognition with the aid of recognized text, where we are the first to report multi-modal results on the IMET set. Results show that text helps for object class recognition if the text is not uniquely coupled to individual object instances.