In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images. The framework is based on Faster R-CNN [1] architecture. First, we use the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations. Second, for each axis-aligned text box proposed by RPN, we extract its pooled features with different pooled sizes and the concatenated features are used to simultaneously predict the text/non-text score, axis-aligned box and inclined minimum area box. At last, we use an inclined non-maximum suppression to get the detection results. Our approach achieves competitive results on text detection benchmarks: ICDAR 2015 and ICDAR 2013.
@article{arxiv.1706.09579,
title = {R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection},
author = {Yingying Jiang and Xiangyu Zhu and Xiaobing Wang and Shuli Yang and Wei Li and Hua Wang and Pei Fu and Zhenbo Luo},
journal= {arXiv preprint arXiv:1706.09579},
year = {2017}
}