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CNBCC: cubic non-uniform B-spline closed curve for arbitrary shape text detection

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Abstract

With the development of deep learning, the performance and efficiency of text detection in natural scenes have been significantly improved. Due to the irregular geometric shape of natural scene text, it is challenging to detect text of arbitrary shape. Most of the existing methods are regression-based or segmentation-based methods. This paper presents an efficient framework to detect arbitrary shape text instances by combining regression-based and segmentation-based methods. Specifically, we use cubic non-uniform B-spline closed curve to fit the boundaries of arbitrary-shaped text instances. By adopting the anchor-free method as the regression detector to obtain the coordinates of B-spline curve control points, and using the segmentation method to obtain the knot vector value, our method not only uses the detection efficiency of regression method, but also combines the insensitivity of segmentation method to arbitrary shape text to improve the accuracy of text detection. Experiments on ICAR2015, CTW1500 and total-text benchmarks, including regular shape and arbitrary shape scene text in natural images, demonstrate the effectiveness of the proposed method.

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References

  1. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint (2014) arXiv:1409.1556

  2. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer (2016)

  3. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, p. 28 (2015)

  4. Wang, W., Xie, E., Li, X., Hou, W., Lu, T., Yu, G., Shao, S.: Shape robust text detection with progressive scale expansion network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9336–9345 (2019a)

  5. Liu, Y., Chen, H., Shen, C., He, T., Jin, L., Wang, L.: ABCNet: real-time scene text spotting with adaptive bezier-curve network. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

  6. Karatzas, D., Gomez-Bigorda, L., Nicolaou, A., Ghosh, S., Bagdanov, A., Iwamura, M., Matas, J., Neumann, L., Chandrasekhar, V.R., Lu, S., et al.: Icdar 2015 competition on robust reading. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1156–1160. IEEE (2015)

  7. Yuliang, L., Lianwen, J., Shuaitao, Z., Sheng, Z.: Detecting curve text in the wild: New dataset and new solution. arXiv preprint arXiv:1712.02170 (2017)

  8. Ch’ng, C.K., Chan, C.S.: Total-text: a comprehensive dataset for scene text detection and recognition. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol. 1, pp. 935–942. IEEE (2017)

  9. Liao, M., Shi, B., Bai, X., Wang, X., Liu, W.: Textboxes: a fast text detector with a single deep neural network. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

  10. Liao, M., Shi, B., Bai, X.: Textboxes++: a single-shot oriented scene text detector. IEEE Trans. Image Process. 27(8), 3676–3690 (2018)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  12. Zhong, Z., Sun, L., Huo, Q.: An anchor-free region proposal network for faster r-cnn-based text detection approaches. Int. J. Doc. Anal. Recognit. (IJDAR) 22(3), 315–327 (2019)

    Article  Google Scholar 

  13. Deng, D., Liu, H., Li, X., Cai, D.: Pixellink: detecting scene text via instance segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

  14. Wang, W., Xie, E., Song, X., Zang, Y., Wang, W., Lu, T., Yu, G., Shen, C.: Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In: IEEE (2019b)

  15. Liao, M., Wan, Z., Yao, C., Chen, K., Bai, X.: Real-time scene text detection with differentiable binarization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11474–11481 (2020)

  16. He, K., Zhang, X., Ren, S., Jian, S.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (2016)

  17. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

  18. Tian, Z., Shen, C., Chen, H., and He, T.: Fcos: Fully convolutional one-stage object detection. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2020)

  19. Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2315–2324 (2016)

  20. Shi, B., Bai, X., Belongie, S.: Detecting oriented text in natural images by linking segments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2550–2558 (2017)

  21. Liu, Z., Lin, G., Yang, S., Feng, J., Lin, W., Goh, W.L.: Learning Markov clustering networks for scene text detection. arXiv preprint arXiv:1805.08365 (2018b)

  22. He, W., Zhang, X.-Y., Yin, F., Liu, C.-L.: Deep direct regression for multi-oriented scene text detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 745–753 (2017)

  23. Hu, H., Zhang, C., Luo, Y., Wang, Y., Han, J., Ding, E.: Wordsup: exploiting word annotations for character based text detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4940–4949 (2017)

  24. Lyu, P., Yao, C., Wu, W., Yan, S., Bai, X.: Multi-oriented scene text detection via corner localization and region segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7553–7563 (2018)

  25. Tang, J., Yang, Z., Wang, Y., Zheng, Q., Xu, Y., Bai, X.: Seglink++: detecting dense and arbitrary-shaped scene text by instance-aware component grouping. Pattern Recognit. 96, 106954 (2019)

    Article  Google Scholar 

  26. Liao, M., Zhu, Z., Shi, B., Xia, G.-s., Bai, X.: Rotation-sensitive regression for oriented scene text detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5909–5918 (2018b)

  27. Xu, Y., Wang, Y., Zhou, W., Wang, Y., Yang, Z., Bai, X.: Textfield: learning a deep direction field for irregular scene text detection. IEEE Trans. Image Process. 28(11), 5566–5579 (2019)

    Article  MathSciNet  Google Scholar 

  28. Xue, C., Lu, S., Zhang, W.: Msr: multi-scale shape regression for scene text detection. arXiv preprint arXiv:1901.02596 (2019)

  29. Long, S., Ruan, J., Zhang, W., He, X., Wu, W., Yao, C.: Textsnake: A flexible representation for detecting text of arbitrary shapes. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 20–36 (2018)

  30. He, T., Tian, Z., Huang, W., Shen, C., Qiao, Y., Sun, C.: An end-to-end textspotter with explicit alignment and attention. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5020–5029 (2018)

  31. Dai, Y., Huang, Z., Gao, Y., Xu, Y., Chen, K., Guo, J., Qiu, W.: Fused text segmentation networks for multi-oriented scene text detection. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3604–3609. IEEE (2018)

  32. Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., Yan, J.: Fots: fast oriented text spotting with a unified network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5676–5685 (2018a)

  33. Tang, Q., Feng, X., Zhang, X.: A spatial feature adaptive network for text detection. Multimedia Tools Appl. 81(11), 15285–15302 (2022)

    Article  Google Scholar 

  34. Yang, C., Chen, M., Xiong, Z., Yuan, Y., Wang, Q.: Cm-net: concentric mask based arbitrary-shaped text detection. IEEE Trans. Image Process. 31, 2864–2877 (2022)

    Article  Google Scholar 

  35. Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network (2016)

  36. Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., Liang, J.: East: an efficient and accurate scene text detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5551–5560 (2017)

  37. Xu, Y., Wang, Y., Zhou, W., Wang, Y., Bai, X.: Textfield: learning a deep direction field for irregular scene text detection. IEEE Trans. Image Process. (2019a)

  38. Zhu, Y., Du, J.: Textmountain: accurate scene text detection via instance segmentation. Pattern Recognit. 110, 107336 (2021)

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Authors and Affiliations

  1. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    Chao Zhu, Benshun Yi & Laigan Luo

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  1. Chao Zhu
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  2. Benshun Yi
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Correspondence to Benshun Yi.

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Zhu, C., Yi, B. & Luo, L. CNBCC: cubic non-uniform B-spline closed curve for arbitrary shape text detection. Vis Comput 40, 3023–3032 (2024). https://doi.org/10.1007/s00371-023-03005-7

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