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Detecting multi-oriented text with corner-based region proposals
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DOI：10.1016/j.neucom.2019.01.013.png)
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Abstract
En 中文
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. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Multi-oriented text detection
Dual-Rol Pooling
Corner-based region proposal network
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