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Scale robust deep oriented-text detection network
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DOI：10.1016/j.patcog.2019.107180.png)
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Abstract
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Text detection is a prerequisite of text recognition, and multi-oriented text detection is a hot topic recently. The existing multi-oriented text detection methods fall short when facing two issues: 1) text scales change in a wide range, and 2) there exists the foreground-background class imbalance. In this paper, we propose a scale-robust deep multi-oriented text-detection model, which not only has the efficiency of the one-stage deep detection model, but also has the comparable accuracy of the two-stage deep text-detection model. We design the feature refining block to fuse multi-scale context features for the purpose of keeping text detection in a higher-resolution feature map. Moreover, in order to mitigate the foreground-background class imbalance, Focal Loss is adopted to up weight the hard-classified samples. Our method is implemented on four benchmark text datasets: ICDAR2013, ICDAR2015, COCOText and MSRA-TD500. The experimental results demonstrate that our method is superior to the existing one-stage deep text-detection models and comparable to the state-of-the-art text detection methods. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Scene text detection
One-stage
Scale robust
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