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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ma, J., Shao, W., Ye, H., Wang, L., Wang, H., Zheng, Y., Xue, X.: Arbitrary-oriented scene text detection via rotation proposals. arXiv preprint arXiv:1703.01086 (2017)
Shi, B., Bai, X., Belongie, S.: Detecting oriented text in natural images by linking segments. In: CVPR (2017)
Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., Liang, J.: EAST: an efficient and accurate scene text detector. In: CVPR (2017)
Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 56–72. Springer, Cham (2016). doi:10.1007/978-3-319-46484-8_4
Huang, W., Qiao, Y., Tang, X.: Robust scene text detection with convolution neural network induced MSER trees. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 497–511. Springer, Cham (2014). doi:10.1007/978-3-319-10593-2_33
Liu, Y., Jin, L.: Deep matching prior network: toward tighter multi-oriented text detection. In: CVPR (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Zhang, Z., Zhang, C., Shen, W., Yao, C., Liu, W., Bai, X.: Multi-oriented text detection with fully convolutional networks. In: CVPR (2016)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
Tian, S., Pan, Y., Huang, C., Lu, S., Yu, K., Tan, C.L.: Text flow: a unified text detection system in natural scene images. In: ICCV (2015)
Busta, M., Neumann, L., Matas, J.: Fastext: efficient unconstrained scene text detector. In: ICCV (2015)
Yin, X.C., Pei, W.Y., Zhang, J., Hao, H.W.: Multi-orientation scene text detection with adaptive clustering. PAMI 37(9), 1930–1937 (2015)
Yin, X.C., Yin, X., Huang, K., Hao, H.W.: Robust text detection in natural scene images. PAMI 36(5), 970–983 (2014)
Li, Y., Jia, W., Shen, C., van den Hengel, A.: Characterness: an indicator of text in the wild. IEEE Trans. Image Process. 23(4), 1666–1677 (2014)
Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: CVPR (2010)
Wang, K., Belongie, S.: Word spotting in the wild. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 591–604. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15549-9_43
Bissacco, A., Cummins, M., Netzer, Y., Neven, H.: PhotoOCR: reading text in uncontrolled conditions. In: ICCV (2013)
Jaderberg, M., Vedaldi, A., Zisserman, A.: Deep features for text spotting. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 512–528. Springer, Cham (2014). doi:10.1007/978-3-319-10593-2_34
Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: Detecting texts of arbitrary orientations in natural images. In: CVPR (2012)
Liao, M., Shi, B., Bai, X., Wang, X., Liu, W.: TextBoxes: a fast text detector with a single deep neural network. In: AAAI (2017)
Zhu, S., Zanibbi, R.: A text detection system for natural scenes with convolutional feature learning and cascaded classification. In: CVPR, pp. 625–632 (2016)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)
Girshick, R.: Fast R-CNN. In: ICCV (2015)
Kang, L., Li, Y., Doermann, D.: Orientation robust text line detection in natural images. In: CVPR (2014)
Mao, J., Li, H., Zhou, W., Yan, S., Tian, Q.: Scale based region growing for scene text detection. In: Proceedings of ACM International Conference on Multimedia (ACM MM) (2013)
Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: CVPR (2016)
Yao, C., Bai, X., Liu, W.: A unified framework for multioriented text detection and recognition. IEEE Trans. Image Process. 23, 4737–4749 (2014)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-69923-3_79
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69922-6
Online ISBN: 978-3-319-69923-3
eBook Packages: Computer ScienceComputer Science (R0)