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arXiv · 1804.02690

Detecting Multi-Oriented Text with Corner-based Region Proposals

Linjie Deng·Yanxiang Gong·Yi Lin·Jingwen Shuai·Xiaoguang Tu·Yuefei Zhang·Zheng Ma·Mei Xie
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Abstract

Previous approaches for scene text detection usually rely on manually defined sliding windows. This work presents an intuitive two-stage region-based method to detect multi-oriented text without any prior knowledge regarding the textual shape. In the first stage, we estimate the possible locations of text instances by detecting and linking corners instead of shifting a set of default anchors. The quadrilateral proposals are geometry adaptive, which allows our method to cope with various text aspect ratios and orientations. In the second stage, we design a new pooling layer named Dual-RoI Pooling which embeds data augmentation inside the region-wise subnetwork for more robust classification and regression over these proposals. Experimental results on public benchmarks confirm that the proposed method is capable of achieving comparable performance with state-of-the-art methods. The code is publicly available at https://github.com/xhzdeng/crpn

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Linjie Deng, Yanxiang Gong, Yi Lin, Jingwen Shuai, Xiaoguang Tu, Yuefei Zhang, Zheng Ma, Mei Xie. 2019-06-03. Detecting Multi-Oriented Text with Corner-based Region Proposals. https://doi.org/10.1016/j.neucom.2019.01.013

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