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Table Detection Using Boundary Refining via Corner Locating

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Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11857))

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Abstract

Table detection based on bounding-box method has achieved remarkable results. However, there still exists inaccurate table boundary locating. In this paper, a table detection method is proposed. Firstly, coarse table detection is implemented through Faster R-CNN. Secondly, corner locating is implemented through RPN and refined through Fast R-CNN. Corner grouping and filtering are implemented through post-processing algorithms. Therefore, unreliable corners are filtered. Thirdly, table boundaries are refined via reliable corners. Experimental results show that the proposed method obviously improves the precision of table boundary locating. We test on ICDAR2017 POD dataset, our method achieves an F-measure of 95.3%. Compared to Faster R-CNN method, the proposed method significantly increases by 3.2% in F-measure. Moreover, our method increases by 3.3% at pixel-level localization.

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References

  1. Sankhe, P.D., Patil, M., Margaret, M.: Deblurring of grayscale images using inverse and Wiener filter. In: International Conference & Workshop on Emerging Trends in Technology, pp. 145–148. ACM (2011)

    Google Scholar 

  2. Antal, G., Martinez, R., Csonka, F., Sbert, M., Szirmay-Kalos, L.: Combining global and local global-illumination algorithms. In: 19th Spring Conference on Computer Graphics, pp. 185–192. ACM (2003)

    Google Scholar 

  3. Rahman, M., Gustafson, S., Irani, P., Subramanian, S.: Tilt techniques: investigating the dexterity of wrist-based input. In: 27th International Conference on Human Factors in Computing Systems, Boston, MA, USA, pp. 1943–1952. ACM (2009)

    Google Scholar 

  4. Chang, F., Chu, S.Y., Chen, C.Y.: Chinese document layout analysis using an adaptive regrouping strategy. Pattern Recogn. 38(2), 261–271 (2005)

    Article  Google Scholar 

  5. Xi, J., Hu, J., Wu, L.: Page segmentation of Chinese newspapers. Pattern Recogn. 35(12), 2695–2704 (2002)

    Article  Google Scholar 

  6. Ha, J., Phillips, I.T., Haralick, R.M.: Document page decomposition using bounding boxes of connected components of black pixels. In: Proceedings of SPIE - The International Society for Optical Engineering, pp. 140–151 (1995)

    Google Scholar 

  7. Sauvola, J., Pietikainen, M.: Page segmentation and classification using fast feature extraction and connectivity analysis. In: 3rd International Conference on Document Analysis and Recognition, pp. 1127–1131. IEEE (1995)

    Google Scholar 

  8. Etemad, K., Doermann, D., Chellappa, R.: Multiscale segmentation of unstructured document pages using soft decision integration. IEEE Trans. Pattern Anal. Mach. Intell. 19(1), 92–96 (1997)

    Article  Google Scholar 

  9. He, K., Gkioxari, G., Dollr, P., Girshick, R.: Mask R-CNN. In: IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  10. 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, pp. 91–99 (2015)

    Google Scholar 

  11. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  12. Gao, L., Yi, X., Jiang, Z., Hao, L., Tang, Z.: ICDAR 2017 competition on page object detection. In: 14th IAPR International Conference on Document Analysis and Recognition, pp. 1417–1422. IEEE (2017)

    Google Scholar 

  13. Kieninger, T., Dengel, A.: Table recognition and labeling using intrinsic layout features. In: Singh, S. (ed.) International Conference on Advances in Pattern Recognition, pp. 307–316. Springer, London (1999). https://doi.org/10.1007/978-1-4471-0833-7_31

    Chapter  Google Scholar 

  14. Kieninger, T., Dengel, A.: Applying the T-RECS table recognition system to the business letter domain. In: 6th International Conference on Document Analysis and Recognition, pp. 518–522. IEEE (2001)

    Google Scholar 

  15. Cesarini, F., Marinai, S., Sarti, L., Soda, G.: Trainable table location in document images. In: International Conference on Pattern Recognition, pp. 236–240. IEEE (2002)

    Google Scholar 

  16. Gatos, B., Danatsas, D., Pratikakis, I., Perantonis, S.J.: Automatic table detection in document images. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3686, pp. 609–618. Springer, Heidelberg (2005). https://doi.org/10.1007/11551188_67

    Chapter  Google Scholar 

  17. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Computer Society (2014)

    Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2014)

    Article  Google Scholar 

  19. Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  20. Vo, N.D., Nguyen, K., Nguyen, T.V., Nguyen, K.: Ensemble of deep object detectors for page object detection. In: 12th International Conference on Ubiquitous Information Management and Communication, p. 11. ACM (2018)

    Google Scholar 

  21. Tychsen-Smith, L., Petersson, L.: DeNet: scalable real-time object detection with directed sparse sampling. In: IEEE International Conference on Computer Vision (ICCV), pp. 428–436. IEEE Computer Society (2017)

    Google Scholar 

  22. Wang, X., Chen, K., Huang, Z., Yao, C., Liu, W.: Point linking network for object detection. arXiv preprint arXiv:1706.03646 (2017)

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

    Google Scholar 

  24. Law, H., Deng, J.: CornerNet: detecting objects as paired keypoints. In: European Conference on Computer Vision (ECCV), pp. 734–750 (2018)

    Google Scholar 

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Acknowledgment

This work was supported by the Natural Science Foundation of Tianjin (Grant No. 18JCYBJC85000).

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Authors and Affiliations

  1. Tianjin Normal University, No. 393 Binshuixi Road, Xiqing District, Tianjin, China

    Ningning Sun, Yuanping Zhu & Xiaoming Hu

Authors
  1. Ningning Sun
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  2. Yuanping Zhu
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  3. Xiaoming Hu
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Corresponding author

Correspondence to Yuanping Zhu .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University, Nanjing, Jiangsu, China

    Jian Yang

  4. Xidian University, Xi'an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

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Sun, N., Zhu, Y., Hu, X. (2019). Table Detection Using Boundary Refining via Corner Locating. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-31654-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31653-2

  • Online ISBN: 978-3-030-31654-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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