Abstract
Table Structure Recognition is an essential part of end-to-end tabular data extraction in document images. The recent success of deep learning model architectures in computer vision remains to be non-reflective in table structure recognition, largely because extensive datasets for this domain are still unavailable while annotating new data is expensive and time-consuming. Traditionally, in computer vision, these challenges are addressed by standard augmentation techniques that are based on image transformations like color jittering and random cropping. As demonstrated by our experiments, these techniques are not effective for the task of table structure recognition. In this paper, we propose TabAug, a re-imagined Data Augmentation technique that produces structural changes in table images through replication and deletion of rows and columns. It also consists of a data-driven probabilistic model that allows control over the augmentation process. To demonstrate the efficacy of our approach, we perform experimentation on ICDAR 2013 dataset where our approach shows consistent improvements in all aspects of the evaluation metrics, with cell-level correct detections improving from 92.16% to 96.11% over the baseline.
U. Khan and S. Zahid—These authors have contributed equally.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arif, S., Shafait, F.: Table detection in document images using foreground and background features. Digital Image Comput. Tech. Appl. 2018, 1–8 (2018)
Bansal, A., Harit, G., Dutta Roy, S.: Table extraction from document images using fixed point model. In: ICVGIP 2014: Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing, pp. 1–8 (2014)
Chen, J., Lopresti, D.: Table detection in noisy off-line handwritten documents. In: 2011 International Conference on Document Analysis and Recognition, Beijing, China, pp. 399–403 (2011)
Dwibedi, D., Misra, I., Hebert, M.: Cut, paste and learn: surprisingly easy synthesis for instance detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1310–1319 (2017)
Fang, H., Sun, J., Wang, R., Gou, M., Li, Y., Lu, C.: InstaBoost: boosting instance segmentation via probability map guided copy-pasting. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 682–691 (2019)
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
Ghiasi, G., et al.: Simple copy-paste is a strong data augmentation method for instance segmentation. ArXiv (2020)
Gilani, A., Qasim, S.R., Malik, I., Shafait, F.: Table detection using deep learning. In: 14th International Conference on Document Analysis and Recognition, pp. 771–776 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Kasar, T., Barlas, P., Adam, S., Chatelain, C., Paquet, T.: Learning to detect tables in scanned document images using line information. In: Twelfth International Conference on Document Analysis and Recognition, pp. 1185–1189 (2013)
Kieninger, T., Dengel, A.: A paper-to-HTML table converting system. In: Proceedings of Document Analysis Systems, pp. 356–365 (1998)
Kieninger, T., Dengel, A.: Table recognition and labeling using intrinsic layout features. In: International Conference on Advances in Pattern Recognition, pp. 307–316 (1999)
Kieninger, T., Dengel, A.: Applying the T-Recs table recognition system to the business letter domain. In: International Conference on Document Analysis and Recognition, p. 0518 (2001)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25 (2012)
Pyreddy, P., Croft, W.B.: TINTI: a system for retrieval in text tables TITLE2: Technical report, University of Massachusetts, USA (1997)
Qasim, S.R., Mahmood, H., Shafait, F.: Rethinking table recognition using graph neural networks. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 142–147 (2019)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2015)
Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: DeepDeSRT: deep learning for detection and structure recognition of tables in document images. In: Fourteenth International Conference on Document Analysis and Recognition, vol. 1, pp. 1162–1167 (2017)
Shafait, F., Smith, R.: Table detection in heterogeneous documents. In: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, pp. 65–72. Document analysis systems (2010)
Shahab, A., Shafait, F., Kieninger, T., Dengel, A.: An open approach towards the benchmarking of table structure recognition systems. In: Document Analysis Systems, pp. 113–120 (2010)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)
Siddiqui, S., Malik, M., Agne, S., Dengel, A., Ahmed, S.: DeCNT: deep deformable CNN for table detection. IEEE Access 6, 74151–74161 (2018)
Tensmeyer, C., Morariu, V.I., Price, B., Cohen, S., Martinez, T.: Deep splitting and merging for table structure decomposition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 114–121 (2019)
Tupaj, S., Shi, Z., Chang, D.H.: Extracting tabular information from text files. In: EECS Department, Tufts University (1996)
Zanibbi, R., Blostein, D., Cordy, J.: A survey of table recognition. IJDAR 7, 1–16 (2004)
Acknowledgement
This work has been partially funded by the Higher Education Commission of Pakistan’s grant for National Center of Artificial Intelligence (NCAI).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Khan, U., Zahid, S., Ali, M.A., Ul-Hasan, A., Shafait, F. (2021). TabAug: Data Driven Augmentation for Enhanced Table Structure Recognition. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-86331-9_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86330-2
Online ISBN: 978-3-030-86331-9
eBook Packages: Computer ScienceComputer Science (R0)