Abstract
CNN has given the state-of-the-art results in computer vision and natural language processing (NLP) domain problems. This has motivated researchers to use deep learning-based techniques for document layout analysis. Due to recent advances in communication and in information technology, methods of data storage, extraction and processing are rapidly evolving. In an information space, there is a large volume of digital documents (DD) already available, and more DD is created continuously. The DD can be natural images, scanned documents, mails, books, archives, etc. Processing, extraction and understanding of relevant information from these DD have prime business importance. Information in DD is present in the form of tables, text, figure, images, diagram, etc. Document images (DI) are DD which are present in the form of images. It is especially office, scanned documents. Recent progress in artificial intelligence has created a growing expectation for automation of data extraction from DD. Tables are frequently present in DI. There is a desperate need to handle tabular information present in DI; otherwise, this information will remain unused and there is high possibility that it will be lost. In this work, we present weakly supervised learning-based approach to detect and recognize the location of the table. The novelty of our work is that it did not require bounding box annotations for table detection. Application of our technique on ICDAR 2013 test data has demonstrated encouraging results.
Similar content being viewed by others
Change history
28 September 2023
A Correction to this paper has been published: https://doi.org/10.1007/s42979-023-02168-3
References
Schreiber S et al. DeepDeSRT: deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol 01; 2017. p. 1162–7.
Gilani A et al. Table detection using deep learning. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol 01; 2017. p. 771–6.
Wang Y, Phillip I, Haralick R. Automatic table ground truth generation and a background-analysis-based table structure extraction method. In: Proceedings sixth international conference on document analysis and recognition. IEEE; 2001. p. 528–32.
Kieninger T, Dengel A. A paper-to-html table converting system. In: Proceedings of document analysis systems (DAS), vol 98; 1998.
Kieninger T, Dengel A. Table recognition and labeling using intrinsic layout features. In: Singh S, editor. International conference on advances in pattern recognition. London: Springer; 1999.
Kieninger T, Dengel A. Applying the t-recs table recognition system to the business letter domain. In: Proceedings sixth international conference on document analysis and recognition. IEEE; 2001. p. 518–22.
Fang J, Gao L, Bai K, Qiu R, Tao X, Tang Z. A table detection method for multipage pdf documents via visual separators and tabular structures. In: 2011 international conference on document analysis and recognition (ICDAR). IEEE; 2011. p. 779–83.
Shafait F, Smith R. Table detection in heterogeneous documents. In: Proceedings of the 9th IAPR international workshop on document analysis systems. ACM; 2010. p. 65–72.
Hao L, Gao L, Yi X, Tang Z. A table detection method for pdf documents based on convolutional neural networks. In: 12th IAPR workshop on document analysis systems (DAS). IEEE; 2016. p. 287–92.
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. Learning deep features for discriminative localization. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV; 2016. p. 2921–9.
Bergamo A, Bazzani L, Anguelov D, Torresani L. Self-taught object localization with deep networks. arXiv preprint arXiv:1409.3964; 2014.
Cinbis RG, Verbeek J, Schmid C. Weakly supervised object localization with multi-fold multiple instance learning. IEEE Trans Pattern Anal Mach Intell. 2015;39:189–203.
Oquab M, Bottou L, Laptev I, Sivic J. Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the CVPR; 2014.
Oquab M, Bottou L, Laptev I, Sivic J. Is object localization for free? Weakly-supervised learning with convolutional neural networks. Proceedings of the CVPR; 2015.
Rong L, Mengyi E, Jianqiang L, Haibin Z. Weakly supervised text attention network for generating text proposals in scene images. In: Proceedings of 14th international conference on document analysis and recognition (ICDAR2017).
Lin M, Chen Q, Yan S. Network in network. In: International conference on learning representations; 2014.
Gatos B, Danatsas D, Pratikakis I, Perantonis SJ. Automatic table detection in document images. In: Singh S, Singh M, Apte C, Perner P, editors. Pattern recognition and data mining. ICAPR. Lecture notes in computer science, vol 3686. Berlin: Springer; 2005.
Thotreingam K et al. Learning to detect tables in scanned document images using line information; 2013.
Liu W et al. SSD: single shot multibox detector. ECCV; 2016.
Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes C, Lee DD, Sugiyama M, Garnett R, editors. Proceedings of the 28th international conference on neural information processing systems (NIPS’15), vol. 1. Cambridge, MA: MIT Press; 2015. p. 91–9.
Yildiz B et al. pdf2table: a method to extract table information from PDF files. In: IICAI; 2005.
Perez-Arriaga MO et al. TAO: system for table detection and extraction from PDF documents. In: FLAIRS conference; 2016.
Shahab A: Table ground truth for the UW3 and UNLV datasets. http://www.iapr-tc11.org/mediawiki/index. Accessed 7 April 2017.
Kavasidis I et al. A saliency-based convolutional neural network for table and chart detection in digitized documents. CoRR abs/1804.06236; 2018.
Singh KK, Lee YJ. Hide-and-seek: a data augmentation technique for weakly-supervised localization and beyond, international conference on computer vision (ICCV); 2017.
He K et al. Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR); 2016. p. 770–778.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “Advances in Computational Intelligence, Paradigms and Applications” guest edited by Young Lee and S. Meenakshi Sundaram.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gurav, A.A., Nene, M.J. Weakly Supervised Learning-based Table Detection. SN COMPUT. SCI. 1, 90 (2020). https://doi.org/10.1007/s42979-020-0113-x
Published:
DOI: https://doi.org/10.1007/s42979-020-0113-x