Abstract
Automated document analysis and parsing has been the focus of research since a long time. An important component of document parsing revolves around understanding tabular regions with respect to their structure identification, followed by precise information extraction. While substantial effort has gone into table detection and information extraction from documents, table structure recognition remains to be a long-standing task demanding dedicated attention. The identification of the table structure enables extraction of structured information from tabular regions which can then be utilized for further applications. To this effect, this research proposes a novel table structure recognition pipeline consisting of row identification and column identification modules. The column identification module utilizes a novel Column Detector Encoder-Decoder model (termed as CoDec Encoder Decoder) which is trained via a novel loss function for predicting the column mask for a given input image. Experiments have been performed to analyze the different components of the proposed pipeline, thus supporting their inclusion for enhanced performance. The proposed pipeline has been evaluated on the challenging ICDAR 2013 table structure recognition dataset, where it demonstrates state-of-the-art performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)
Coüasnon, B., Lemaitre, A.: Recognition of tables and forms. In: Handbook of Document Image Processing and Recognition (2014). https://doi.org/10.1007/978-0-85729-859-1
Embley, D.W., Hurst, M., Lopresti, D., Nagy, G.: Table-processing paradigms: a research survey. Int. J. Doc. Anal. Recogn. 8(2–3), 66–86 (2006)
Fang, J., Tao, X., Tang, Z., Qiu, R., Liu, Y.: Dataset, ground-truth and performance metrics for table detection evaluation. In: International Workshop on Document Analysis Systems, pp. 445–449 (2012)
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
Gilani, A., Qasim, S.R., Malik, I., Shafait, F.: Table detection using deep learning. In: International Conference on Document Analysis and Recognition, pp. 771–776 (2017)
Göbel, M., Hassan, T., Oro, E., Orsi, G.: ICDAR 2013 table competition. In: International Conference on Document Analysis and Recognition, pp. 1449–1453 (2013)
Hao, L., Gao, L., Yi, X., Tang, Z.: A table detection method for pdf documents based on convolutional neural networks. In: IAPR Workshop on Document Analysis Systems, pp. 287–292 (2016)
Hu, J., Kashi, R.S., Lopresti, D.P., Wilfong, G.: Medium-independent table detection. In: Document Recognition and Retrieval VII, vol. 3967, pp. 291–302 (1999)
Illingworth, J., Kittler, J.: A survey of the Hough transform. Comput. Vis. Graph. Image Process. 44(1), 87–116 (1988)
Khusro, S., Latif, A., Ullah, I.: On methods and tools of table detection, extraction and annotation in pdf documents. J. Inf. Sci. 41(1), 41–57 (2015)
Kieninger, T., Dengel, A.: The T-Recs table recognition and analysis system. In: International Workshop on Document Analysis Systems, pp. 255–270 (1998)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: Tablebank: table benchmark for image-based table detection and recognition. In: Language Resources and Evaluation Conference, pp. 1918–1925 (2020)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: International Conference on International Conference on Machine Learning, pp. 807–814 (2010)
Paliwal, S.S., Vishwanath, D., Rahul, R., Sharma, M., Vig, L.: TableNet: deep learning model for end-to-end table detection and tabular data extraction from scanned document images. In: International Conference on Document Analysis and Recognition, pp. 128–133 (2019)
Paszke, A., et al.: Automatic differentiation in PyTorch (2017)
Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: CascadeTabNet: an approach for end to end table detection and structure recognition from image-based documents. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 572–573 (2020)
Qasim, S.R., Mahmood, H., Shafait, F.: Rethinking table recognition using graph neural networks. In: International Conference on Document Analysis and Recognition, pp. 142–147 (2019)
Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: DeepDesrt: deep learning for detection and structure recognition of tables in document images. In: International Conference on Document Analysis and Recognition, vol. 1, pp. 1162–1167 (2017)
Shigarov, A., Mikhailov, A., Altaev, A.: Configurable table structure recognition in untagged pdf documents. In: ACM Symposium on Document Engineering, pp. 119–122 (2016)
Siddiqui, S.A., Khan, P.I., Dengel, A., Ahmed, S.: Rethinking semantic segmentation for table structure recognition in documents. In: International Conference on Document Analysis and Recognition, pp. 1397–1402 (2019)
Siddiqui, S.A., Malik, M.I., Agne, S., Dengel, A., Ahmed, S.: DeCNT: deep deformable CNN for table detection, vol. 6, pp. 74 151–74 161 (2018)
e Silva, A.C., Jorge, A.M., Torgo, L.: Design of an end-to-end method to extract information from tables. Int. J. Doc. Anal. Recogn. (IJDAR) 8(2–3), 144–171 (2006)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Smith, R.: An overview of the Tesseract OCR engine. In: International Conference on Document Analysis and Recognition, vol. 2, pp. 629–633 (2007)
Wang, Y., Phillips, I.T., Haralick, R.M.: Table structure understanding and its performance evaluation. Pattern Recogn. 37(7), 1479–1497 (2004)
Wang, Y., Phillips, I., Haralick, R.: Automatic table ground truth generation and a background-analysis-based table structure extraction method. In: International Conference on Document Analysis and Recognition, pp. 528–532 (2001)
Zanibbi, R., Blostein, D., Cordy, J.R.: A survey of table recognition. Doc. Anal. Recogn. 7(1), 1–16 (2004)
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
Pegu, B., Singh, M., Agarwal, A., Mitra, A., Singh, K. (2021). Table Structure Recognition Using CoDec Encoder-Decoder. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12917. Springer, Cham. https://doi.org/10.1007/978-3-030-86159-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-86159-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86158-2
Online ISBN: 978-3-030-86159-9
eBook Packages: Computer ScienceComputer Science (R0)
