Abstract
As global trends are shifting towards data-driven industries, the demand for automated algorithms that can convert images of scanned documents into machine readable information is rapidly growing. In addition to digitization there is an improvement toward process automation that used to require manual inspection of documents. Although optical character recognition (OCR) technologies mostly solved the task of converting human-readable characters from images, the task of extracting tables has been less focused on. This recognition consists of two sub-tasks: table detection and table structure recognition. Most prior work on this problem focuses on either task without offering an end-to-end solution or paying attention to real application conditions like rotated images or noise artefacts. Recent work shows a clear trend towards deep learning using transfer learning for table structure recognition due to the lack of sufficiently large datasets. We present a multistage pipeline named Multi-Type-TD-TSR, which offers an end-to-end solution for table recognition. It utilizes state-of-the-art deep learning models and differentiates between three types of tables based on their borders. For the table structure recognition we use a deterministic non-data driven algorithm, which works on all three types. In addition, we present an algorithm for non-bordered tables and one for bordered ones as the basis of our table structure detection algorithm. We evaluate Multi-Type-TD-TSR on a self annotated subset of the ICDAR 2019 table structure recognition dataset [5] and achieve a new state-of-the-art. Source code is available under https://github.com/Psarpei/Multi-Type-TD-TSR.
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
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Bühler, B., Paulheim, H.: Web table classification based on visual features. CoRR abs/2103.05110 (2021). https://arxiv.org/abs/2103.05110
Cohen, W.W., Hurst, M., Jensen, L.S.: A flexible learning system for wrapping tables and lists in html documents. In: Proceedings of the 11th International Conference on World Wide Web, WWW 2002, pp. 232–241. Association for Computing Machinery, New York (2002). https://doi.org/10.1145/511446.511477
Cortes, C., Vapnik, V.: Support vector machine. Mach. Learn. 20(3), 273–297 (1995)
Gao, L., et al.: ICDAR 2019 competition on table detection and recognition (CTDAR). In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1510–1515 (2019). https://doi.org/10.1109/ICDAR.2019.00243
Gatterbauer, W., Bohunsky, P., Herzog, M., Krüpl, B., Pollak, B.: Towards domain-independent information extraction from web tables. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 71–80. Association for Computing Machinery, New York (2007). https://doi.org/10.1145/1242572.1242583
Gilani, A., Qasim, S.R., Malik, I., Shafait, F.: Table detection using deep learning. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 771–776. IEEE (2017)
Goodfellow, I.J., et al.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)
Göbel, M., Hassan, T., Oro, E., Orsi, G.: ICDAR 2013 table competition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1449–1453 (2013). https://doi.org/10.1109/ICDAR.2013.292
Kasar, T., Barlas, P., Adam, S., Chatelain, C., Paquet, T.: Learning to detect tables in scanned document images using line information. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1185–1189. IEEE (2013)
Kieninger, T., Dengel, A.: The T-recs table recognition and analysis system. In: Lee, S.-W., Nakano, Y. (eds.) DAS 1998. LNCS, vol. 1655, pp. 255–270. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48172-9_21
Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: TableBank: table benchmark for image-based table detection and recognition. In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 1918–1925. European Language Resources Association, Marseille (2020). https://www.aclweb.org/anthology/2020.lrec-1.236
Lu, T., Dooms, A.: Probabilistic homogeneity for document image segmentation. Pattern Recognit. 109, 1–14 (2021). https://doi.org/10.1016/j.patcog.2020.107591
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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 572–573 (2020)
Pyreddi, P., Croft, W.B.: A system for retrieval in text tables. In: ACM DL (1997)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497 (2015)
Reza, M.M., Bukhari, S.S., Jenckel, M., Dengel, A.: Table localization and segmentation using GAN and CNN. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 5, pp. 152–157. IEEE (2019)
Riba, P., Dutta, A., Goldmann, L., Fornés, A., Ramos, O., Lladós, J.: Table detection in invoice documents by graph neural networks. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 122–127 (2019). https://doi.org/10.1109/ICDAR.2019.00028
Rosebrock, A.: Text skew correction with opencv and python (2017). pyImageSearch, https://www.pyimagesearch.com/2017/02/20/text-skew-correction-opencv-python/. Accessed 17 Feb 2021
Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: 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. 1, pp. 1162–1167. IEEE (2017)
Seo, W., Koo, H.I., Cho, N.I.: Junction-based table detection in camera-captured document images. Int. J. Doc. Anal. Recognit. (IJDAR) 18, 1–11 (2014). https://doi.org/10.1007/s10032-014-0226-7
Subramanyam, V.S.: Iou (intersection over union) (2017). Medium. https://medium.com/analytics-vidhya/iou-intersection-over-union-705a39e7acef. Accessed 09 July 2021
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
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
Fischer, P., Smajic, A., Abrami, G., Mehler, A. (2021). Multi-Type-TD-TSR – Extracting Tables from Document Images Using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: From OCR to Structured Table Representations. In: Edelkamp, S., Möller, R., Rueckert, E. (eds) KI 2021: Advances in Artificial Intelligence. KI 2021. Lecture Notes in Computer Science(), vol 12873. Springer, Cham. https://doi.org/10.1007/978-3-030-87626-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-87626-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87625-8
Online ISBN: 978-3-030-87626-5
eBook Packages: Computer ScienceComputer Science (R0)