An anchor-free region proposal network for Faster R-CNN-based text detection approaches


The anchor mechanism of Faster R-CNN and SSD framework is considered not effective enough to scene text detection, which can be attributed to its Intersection-over-Union-based matching criterion between anchors and ground-truth boxes. In order to better enclose scene text instances of various shapes, it requires to design anchors of various scales, aspect ratios and even orientations manually, which makes anchor-based methods sophisticated and inefficient. In this paper, we propose a novel anchor-free region proposal network (AF-RPN) to replace the original anchor-based RPN in the Faster R-CNN framework to address the above problem. Compared with the anchor-based region proposal generation approaches (e.g., RPN, FPN–RPN, RRPN and FPN–RRPN), AF-RPN can get rid of complicated anchor design and achieves higher recall rate on both horizontal and multi-oriented text detection benchmark tasks. Owing to the high-quality text proposals, our Faster R-CNN-based two-stage text detection approach achieves the state-of-the-art results on ICDAR-2017 MLT, COCO-Text, ICDAR-2015 and ICDAR-2013 text detection benchmark tasks by only using single-scale and single-model testing.

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  1. 1.

    Shahab, A., Shafait, F., Dengel, A.: ICDAR 2011 robust reading competition challenge 2: reading text in scene images. In: ICDAR, pp. 1491–1496 (2011)

  2. 2.

    Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., Gomez, L., Mestre, S.R., Mas, J., Mota, D.F., Almazan, J.A., de las Heras, L.P.: ICDAR 2013 robust reading competition. In: ICDAR, pp. 1484–1493 (2013)

  3. 3.

    Karatzas, D., Gomez, L., Nicolaou, A., Ghosh, S., Bagdanov, A., Iwamura, M., Matas, J., Neumann, L., Chandrasekhar, V.R., Lu, S.-J., Shafait, F., Uchida, S., Valveny, E.: ICDAR 2015 robust reading competition. In: ICDAR, pp. 1156–1160 (2015)

  4. 4.

    Nayef, N., Yin, F., Bizid, I., Choi, H., Feng, Y., Karatzas, D., Luo, Z.-B., Pal, U., Rigaud, C., Chazalon, J., Khlif, W., Luqman, M.M., Burie, J.C., Liu, C.-L., Ogier, J.M.: ICDAR2017 robust reading challenge on multi-lingual scene text detection and script identification—RRC-MLT. In: ICDAR, pp. 1454–1459 (2017)

  5. 5.

    Ren, S.-Q., He, K.-M., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. PAMI 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  6. 6.

    Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: Single shot multiBox detector. In: ECCV (2016)

  7. 7.

    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC, pp. 384–393 (2002)

  8. 8.

    Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: CVPR, pp. 2963–2970 (2010)

  9. 9.

    He, W.-H., Zhang, X.-Y., Yin, F., Liu, C.-L.: Deep direct regression for multi-oriented scene text detection. In: ICCV, pp. 745–753 (2017)

  10. 10.

    Zhong, Z.-Y., Jin, L.-W., Huang, S.-P.: DeepText: a new approach for proposal generation and text detection in natural images. In: ICASSP, pp. 1208–1212 (2017)

  11. 11.

    Liao, M.-H., Shi, B.-G., Bai, X., Wang, X.-G., Liu, W.-Y.: TextBoxes: a fast text detector with a single deep neural network. In: AAAI, pp. 4164–4167 (2016)

  12. 12.

    Ma, J.-Q., Shao, W.-Y., Ye, H., Wang, L., Wang, H., Zheng, Y.-B., Xue, X.-Y.: Arbitrary-oriented scene text detection via rotation proposals. IEEE Trans. Multimed. 20(11), 3111–3122 (2018)

    Article  Google Scholar 

  13. 13.

    Liu, Y.-L., Jin, L.-W.: Deep matching prior network toward tighter multi-oriented text detection. In: CVPR, pp. 1962–1969 (2017)

  14. 14.

    Huang, L.-C., Yang, Y., Deng, T.-F., Yu, Y.-N.: Densebox: unifying landmark localization with end to end object detection. Preprint (2015). arXiv:1509.04874

  15. 15.

    Zhou, X.-Y., Yao, C., Wen, H., Wang, Y.-Z., Zhou, S.-C., He, W.-R., Liang, J.-J.: EAST: An efficient and accurate scene text detector. In: CVPR, pp. 5551–5560 (2017)

  16. 16.

    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. PAMI 39(4), 640–651 (2017)

    Article  Google Scholar 

  17. 17.

    Lin, T.-Y., Dollár, P., Girshick, R.B., He, K.-M., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, pp. 2117–2125 (2017)

  18. 18.

    Veit, A., Matera, T., Neumann, L., Matas, J., Belongie, S.: COCO-Text: dataset and benchmark for text detection and recognition in natural images. Preprint (2016). arXiv:1601.07140

  19. 19.

    Neumann, L., Matas, J.: A method for text localization and recognition in real-world images. In: ACCV, pp. 770–783 (2010)

  20. 20.

    Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: CVPR, pp. 3538–3545 (2012)

  21. 21.

    Yin, X.-C., Yin, X.-W., Huang, K.-Z., Hao, H.-W.: Robust text detection in natural scene images. IEEE Trans. PAMI 36(5), 970–983 (2014)

    Article  Google Scholar 

  22. 22.

    Huang, W.-L., Qiao, Y., Tang, X.-O.: Robust scene text detection with convolutional neural networks induced MSER trees. In: ECCV, pp. 497–511 (2014)

  23. 23.

    Sun, L., Huo, Q., Jia, W., Chen, K.: A robust approach for text detection from natural scene images. Pattern Recogn. 48(9), 2906–2920 (2015)

    Article  Google Scholar 

  24. 24.

    Yin, X.-C., Pei, W.-Y., Zhang, J., Hao, H.-W.: Multi-orientation scene text detection with adaptive clustering. IEEE Trans. PAMI 37(9), 1930–1937 (2015)

    Article  Google Scholar 

  25. 25.

    Lu, S.-J., Chen, T., Tian, S.-X., Lim, J.-H., Tan, C.-L.: Scene text extraction based on edges and support vector regression. IJDAR 18(2), 125–135 (2015)

    Article  Google Scholar 

  26. 26.

    Gomez, L., Karatzas, D.: A fast hierarchical method for multi-script and arbitrary oriented scene text extraction. IJDAR 19(4), 335–349 (2016)

    Article  Google Scholar 

  27. 27.

    Fabrizio, J., Robert-Seidowsky, M., Dubuisson, S., Calarasanu, S., Boissel, R.: TextCatcher: a method to detect curved and challenging text in natural scenes. IJDAR 19(2), 99–117 (2016)

    Article  Google Scholar 

  28. 28.

    Gomez, L., Karatzas, D.: TextProposals: a text-specific selective search algorithm for word spotting in the wild. Pattern Recogn. 70, 60–74 (2017)

    Article  Google Scholar 

  29. 29.

    Wang, T., Wu, D.-J., Coates, A., Ng, A.Y.: End-to-end text recognition with convolutional neural networks. In: ICPR, pp. 3304–3308 (2012)

  30. 30.

    Jaderberg, M., Vedaldi, A., Zisserman, A.: Deep features for text spotting. In: ECCV, pp. 512–528 (2014)

  31. 31.

    Zhang, Z., Zhang, C., Shen, W., Yao, C., Liu, W., Bai, X.: Multi-oriented text detection with fully convolutional networks. In: CVPR, pp. 4159–4167 (2016)

  32. 32.

    Yao, C., Bai, X., Sang, N., Zhou, X.-Y., Zhou, S.-C., Cao, Z.-M.: Scene text detection via holistic, multi-channel prediction. Preprint (2016). arXiv:1606.09002

  33. 33.

    Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Reading text in the wild with convolutional neural networks. IJCV 116(1), 1–20 (2016)

    MathSciNet  Article  Google Scholar 

  34. 34.

    Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localization in natural images. In: CVPR, pp. 2315–2324 (2016)

  35. 35.

    Tian, Z., Huang, W.-L., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network. In: ECCV, pp. 56–72 (2016)

  36. 36.

    Shi, B.-G., Bai, X., Belongiey, S.: Detecting oriented text in natural images by linking segments. In: CVPR, pp. 2550–2558 (2017)

  37. 37.

    Hu, H., Zhang, C.-Q., Luo, Y.-X., Wang, Y.-Z., Han, J.-Y., Ding, E.: WordSup: exploiting word annotations for character based text detection. In: ICCV, pp. 4940–4949 (2017)

  38. 38.

    Jung, K., Kim, K., Jain, A.: Text information extraction in images and video: a survey. Pattern Recogn. 37(5), 977–997 (2004)

    Article  Google Scholar 

  39. 39.

    Renton, G., Soullard, Y., Chatelain, C., Adam, S., Kermorvant, C., Paquet, T.: Fully convolutional network with dilated convolutions for handwritten text line segmentation. IJDAR 21(3), 177–186 (2018)

    Article  Google Scholar 

  40. 40.

    Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp. 580–587 (2014)

  41. 41.

    Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779–788 (2016)

  42. 42.

    Deng, D., Liu, H.-F., Li, X.-L., Cai, D.: Pixellink: detecting scene text via instance segmentation. In: AAAI (2018)

  43. 43.

    Lin, T.-Y., Goyal, P., Girshick, R.B., He, K.-M., Dollár, P.: Focal loss for dense object detection. In: ICCV, pp. 2980–2988 (2017)

  44. 44.

    He, K.-M., Gkioxari, G., Dollár, P., Girshick, R.B.: Mask R-CNN. In: ICCV, pp. 2980–2988 (2017)

  45. 45.

    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)

  46. 46.

    He, K.-M., Zhang, X.-Y., Ren, S.-Q., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

  47. 47.

    Girshick, R.B.: Fast R-CNN. In: ICCV (2015)

  48. 48.

    Gomez, R., Shi, B.-G., Gomez, L., Neumann, L., Veit, A., Matas, J., Belongie, S., Karatzas, D.: ICDAR2017 robust reading challenge on COCO-Text. In: ICDAR, pp. 1435–1443 (2017)

  49. 49.

    Liu, X.-B., Liang, D., Yan, S., Chen, D.-G., Qiao, Y., Yan, J.-J.: FOTS: fast oriented text spotting with a unified network. In: CVPR, pp. 5676–5685 (2018)

  50. 50.

    Girshick, R.B., Radosavovic, I., Gkioxari, G., Dollár, P., He, K.-M.: Detectron (2018).

  51. 51.

    Lyu, P.-Y., Yao, C., Wu, W.-H., Yan, S.-C., Bai, X.: Multi-Oriented scene text detection via corner localization and region segmentation. In: CVPR, pp. 7553–7563 (2018)

  52. 52.

    Liao, M.-H., Zhu, Z., Shi, B.-G., Xia, G.-S., Bai, X.: Rotation-sensitive regression for oriented scene text detection. In: CVPR, pp. 5909–5918 (2018)

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Correspondence to Zhuoyao Zhong.

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This work was done when Z. Zhong was an intern in Speech Group, Microsoft Research Asia, Beijing, China.

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Zhong, Z., Sun, L. & Huo, Q. An anchor-free region proposal network for Faster R-CNN-based text detection approaches. IJDAR 22, 315–327 (2019).

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  • Scene text detection
  • Anchor
  • Anchor-free
  • Region proposal generation
  • Faster R-CNN