Abstract
Invoices are so vastly used in business. For each invoice, an employee has to verify carefully written data including date, legal, and the courtesy amount present in each table. However, this task is not only time-consuming but also prone to inaccuracies and errors, especially when it comes to processing a massive amount of invoices. A smart capture system is required to facilitate processing invoices automatically and it is more challenging since relevant data are not narrative but arranged in tables. Although it is true that OCR (Optical Character Recognition) is able to read and capture data, it suffers from inefficiency in table locating and loses structural features of tabular data. Table recognition is widely carried out using deep learning and heuristics and a better result was reached as humans would. In this paper, we present a part of a smart capture system for invoices which is based on table recognition workflow for scanned invoices. This workflow consists of three main steps: the first step is a prepossessing step which is used to enhance the quality of scanned invoices. The second step is a deep learning-based table detection approach where we use DocCutout and DocCutmix for data augmentation. The third step is a heuristic-based table structure recognition approach. The presented approaches are evaluated on public data sets.
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
References
Arif, S., Shafait, F.: Table detection in document images using foreground and background features. In: 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8 (2018)
Boals, S.: The Value of Smart Capture in Digital Transformation. https://ephesoft.com/blog/the-value-of-smart-capture-in-digital-transformation/ (2020)
Cesarini, F., Marinai, S., Sarti, L., Soda, G.: Trainable table location in document images. In: Object Recognition Supported by User Interaction for Service Robots, vol. 3, pp. 236–240. IEEE (2002)
Coüasnon, B., Lemaitre, A.: Recognition of tables and forms (2014)
Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 894–901. IEEE (2019)
DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint (2017)
Embley, D., Hurst, M., Lopresti, D., Nagy, G.: Table-processing paradigms: a research survey. IJDAR 8, 66–86 (2006)
Gao, L., et al.: ICDAR 2019 competition on table detection and recognition (CTDAR). In: International Conference on Document Analysis and Recognition (ICDAR), pp. 1510–1515 (2019)
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 (ICDAR), vol. 1, pp. 1417–1422 (2017)
Gilani, A., Qasim, S.R., Malik, I., Shafait, F.: Table detection using deep learning. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, pp. 771–776 (2017)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Göbel, M., Hassan, T., Oro, E., Orsi, G.: ICDAR 2013 table competition. In: 12th International Conference on Document Analysis and Recognition, pp. 1449–1453 (2013)
Göbel, M., Hassan, T., Oro, E., Orsi, G.: A methodology for evaluating algorithms for table understanding in pdf documents. In: Proceedings of the ACM Symposium on Document Engineering, pp. 45–48 (2012)
Harley, A.W., Ufkes, A., Derpanis, K.G.: Evaluation of deep convolutional nets for document image classification and retrieval. In: International Conference on Document Analysis and Recognition (ICDAR)
He, D., Cohen, S., Price, B., Kifer, D., Giles, C.L.: Multi-scale multi-task FCN for semantic page segmentation and table detection. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 254–261 (2017)
Jahan, M.A.C.A., Ragel, R.G.: Locating tables in scanned documents for reconstructing and republishing. In: 7th International Conference on Information and Automation for Sustainability, pp. 1–6 (2014)
Kieninger, T., Dengel, A.: The T-Recs table recognition and analysis system. In: International Workshop on Document Analysis Systems, pp. 255–270 (1998)
Kieninger, T.G.: Table structure recognition based on robust block segmentation. In: Document Recognition V, vol. 3305, pp. 22–32 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2012)
Lee, Y., Hong, T., Kim, S.: Data augmentations for document images. In: SDU@ AAAI (2021)
Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: TableBank: table benchmark for image-based table detection and recognition. arXiv preprint (2019)
Lopresti, D., Nagy, G.: A tabular survey of automated table processing. In: Chhabra, A.K., Dori, D. (eds.) Graphics Recognition Recent Advances, pp. 93–120. Springer, Berlin Heidelberg (2000). https://doi.org/10.1007/3-540-40953-X_9
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 28, pp. 91–99 (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: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1162–1167 (2017)
Shafait, F., Smith, R.: Table detection in heterogeneous documents, pp. 65–72. New York, NY, USA (2010)
Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: DeepTabStR: deep learning based table structure recognition. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 1403–1409 (2019)
Siddiqui, S.A., Malik, M.I., Agne, S., Dengel, A., Ahmed, S.: DeCNT: deep deformable CNN for table detection. IEEE Access 6, 74151–74161 (2018)
e Silva, A.C.: Learning rich hidden Markov models in document analysis: table location. In: The 10th International Conference on Document Analysis and Recognition, pp. 843–847 (2009)
Tran, D.N., Tran, T.A., Oh, A., Kim, S.H., Na, I.S.: Table detection from document image using vertical arrangement of text blocks. Int. J. Contents 11(4), 77–85 (2015)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks (2017). https://doi.org/10.1109/CVPR.2017.634
Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: CutMix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023–6032 (2019). https://doi.org/10.1109/ICCV.2019.00612
Zanibbi, R., Blostein, D., Cordy, J.: A survey of table recognition: models, observations, transformations, and inferences. Online: https://www.cs.queensu.ca/~cordy/Papers/IJDAR_ Tables.pdf, Last Checked pp. 12–01 (2007)
Acknowledgments
This research and innovation work is supported by MOBIDOC grants from the EU and National Agency for the Promotion of Scientific Research under the AMORI project and in collaboration with Telnet Innovation Labs from Telnet Holding.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kazdar, T., Jmal, M., Souidene, W., Attia, R. (2022). Table Recognition in Scanned Documents. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_58
Download citation
DOI: https://doi.org/10.1007/978-3-031-16014-1_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16013-4
Online ISBN: 978-3-031-16014-1
eBook Packages: Computer ScienceComputer Science (R0)