Abstract
In this paper, we present a novel Orientation-Aware Text Proposals Network (OA-TPN) for detecting text in the wild. The OA-TPN is able to accurately localize arbitrary-oriented text lines in a natural image. Instead of detecting the whole text line at one time, the OA-TPN detects sequences of small-scale orientation-aware text proposals. To handle text lines with different orientations, we utilize deep networks to jointly estimate text proposals with associate directions at the convolutional maps. Final text bounding boxes can be generated from the predicted text proposals by implementing a proposed text-line construction approach. The proposed text detector works reliably on multi-scale and multi-orientation text with single-scale images. Experimental results on the MSRA-TD500 and SWT demonstrate the effectiveness of our methods.
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Acknowledgments
This work was supported in part by National Natural Science Foundation of China (U1613211, 61503367), Shenzhen basic research program (JCYJ20160229193541167, JCYJ20150401145529049), and Guangdong Research Program (2015B010129013, 2015A030310289).
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Huang, H., Tian, Z., He, T., Huang, W., Qiao, Y. (2017). Orientation-Aware Text Proposals Network for Scene Text Detection. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_79
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