Abstract
Tables are everywhere, from scientific journals, papers, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid development of deep learning networks. The goals of this survey are to provide a profound comprehension of the major developments in the field of Table Detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches. Furthermore, we provide an analysis of both classic and new applications in the field. Lastly, the datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature. Finally, we go over the architecture of utilizing various object detection and table structure recognition methods to create an effective and efficient system, as well as a set of development trends to keep up with state-of-the-art algorithms and future research. We have also set up a public GitHub repository where we will be updating the most recent publications, open data, and source code. The GitHub repository is available at https://github.com/abdoelsayed2016/table-detection-structure-recognition.
- Abdelrahman Abdallah, Alexander Berendeyev, Islam Nuradin, and Daniyar Nurseitov. 2022. TNCR:Table net detection and classification dataset. Neurocomputing 473(2022), 79–97. https://doi.org/10.1016/j.neucom.2021.11.101Google ScholarDigital Library
- Abdelrahman Abdallah, Daniel Eberharter, Zoe Pfister, and Adam Jatowt. 2024. Transformers and Language Models in Form Understanding: A Comprehensive Review of Scanned Document Analysis. arXiv preprint arXiv:2403.04080(2024).Google Scholar
- Abdelrahman Abdallah and Adam Jatowt. 2023. Generator-retriever-generator: A novel approach to open-domain question answering. arXiv preprint arXiv:2307.11278(2023).Google Scholar
- Abdelrahman Abdallah, Mahmoud Kasem, Mahmoud Abdalla, Mohamed Mahmoud, Mohamed Elkasaby, Yasser Elbendary, and Adam Jatowt. 2024. ArabicaQA: A Comprehensive Dataset for Arabic Question Answering. arXiv preprint arXiv:2403.17848(2024).Google Scholar
- Madhav Agarwal, Ajoy Mondal, and CV Jawahar. 2021. Cdec-net: Composite deformable cascade network for table detection in document images. In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 9491–9498.Google ScholarCross Ref
- Ahmed Alsayat. 2023. Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca. Neural Computing and Applications 35, 6 (2023), 4701–4722.Google ScholarDigital Library
- Saman Arif and Faisal Shafait. 2018. Table detection in document images using foreground and background features. In 2018 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 1–8.Google Scholar
- Anders Arpteg, Björn Brinne, Luka Crnkovic-Friis, and Jan Bosch. 2018. Software engineering challenges of deep learning. In 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 50–59.Google ScholarCross Ref
- Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35, 8(2013), 1798–1828.Google ScholarDigital Library
- Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 2020. End-to-end object detection with transformers. In European conference on computer vision. Springer, 213–229.Google ScholarDigital Library
- Ángela Casado-García, César Domínguez, Jónathan Heras, Eloy Mata, and Vico Pascual. 2020. The benefits of close-domain fine-tuning for table detection in document images. In International workshop on document analysis systems. Springer, 199–215.Google ScholarCross Ref
- Francesca Cesarini, Simone Marinai, L Sarti, and Giovanni Soda. 2002. Trainable table location in document images. In Object recognition supported by user interaction for service robots, Vol. 3. IEEE, 236–240.Google Scholar
- Surekha Chandran and Rangachar Kasturi. 1993. Structural recognition of tabulated data. In Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93). IEEE, 516–519.Google ScholarCross Ref
- Zewen Chi, Heyan Huang, Heng-Da Xu, Houjin Yu, Wanxuan Yin, and Xian-Ling Mao. 2019. Complicated Table Structure Recognition. arXiv preprint arXiv:1908.04729(2019).Google Scholar
- Bertrand Coüasnon and Aurélie Lemaitre. 2014. Recognition of tables and forms.Google Scholar
- Yuntian Deng, David Rosenberg, and Gideon Mann. 2019. Challenges in end-to-end neural scientific table recognition. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 894–901.Google ScholarCross Ref
- Haoyu Dong, Shijie Liu, Shi Han, Zhouyu Fu, and Dongmei Zhang. 2019. Tablesense: Spreadsheet table detection with convolutional neural networks. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 69–76.Google ScholarDigital Library
- Ana Costa e Silva. 2009. Learning rich hidden markov models in document analysis: Table location. In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 843–847.Google ScholarDigital Library
- David W Embley, Matthew Hurst, Daniel Lopresti, and George Nagy. 2006. Table-processing paradigms: a research survey. International Journal of Document Analysis and Recognition (IJDAR) 8, 2(2006), 66–86.Google ScholarCross Ref
- Rasool Fakoor, Faisal Ladhak, Azade Nazi, and Manfred Huber. 2013. Using deep learning to enhance cancer diagnosis and classification. In Proceedings of the international conference on machine learning, Vol. 28. ACM, New York, USA, 3937–3949.Google Scholar
- Miao Fan and Doo Soon Kim. 2015. Table region detection on large-scale PDF files without labeled data. CoRR, abs/1506.08891(2015).Google Scholar
- Jing Fang, Prasenjit Mitra, Zhi Tang, and C Lee Giles. 2012. Table header detection and classification. In Twenty-Sixth AAAI Conference on Artificial Intelligence.Google Scholar
- Jing Fang, Xin Tao, Zhi Tang, Ruiheng Qiu, and Ying Liu. 2012. Dataset, ground-truth and performance metrics for table detection evaluation. In 2012 10th IAPR International Workshop on Document Analysis Systems. IEEE, 445–449.Google ScholarDigital Library
- Pascal Fischer, Alen Smajic, Giuseppe Abrami, and Alexander Mehler. 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 KI 2021: Advances in Artificial Intelligence: 44th German Conference on AI, Virtual Event, September 27–October 1, 2021, Proceedings 44. Springer, 95–108.Google Scholar
- Liangcai Gao, Yilun Huang, Hervé Déjean, Jean-Luc Meunier, Qinqin Yan, Yu Fang, Florian Kleber, and Eva Lang. 2019. ICDAR 2019 competition on table detection and recognition (cTDaR). In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 1510–1515.Google Scholar
- Liangcai Gao, Xiaohan Yi, Zhuoren Jiang, Leipeng Hao, and Zhi Tang. 2017. ICDAR2017 competition on page object detection. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Vol. 1. IEEE, 1417–1422.Google Scholar
- Arnab Ghosh Chowdhury, Martin ben Ahmed, and Martin Atzmueller. 2022. Towards Tabular Data Extraction From Richly-Structured Documents Using Supervised and Weakly-Supervised Learning. In 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 1–4.Google Scholar
- Azka Gilani, Shah Rukh Qasim, Imran Malik, and Faisal Shafait. 2017. Table detection using deep learning. In 2017 14th IAPR international conference on document analysis and recognition (ICDAR), Vol. 1. IEEE, 771–776.Google Scholar
- Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 2012. A methodology for evaluating algorithms for table understanding in PDF documents. In Proceedings of the 2012 ACM symposium on Document engineering. 45–48.Google ScholarDigital Library
- Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 2013. ICDAR 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition. IEEE, 1449–1453.Google Scholar
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press.Google ScholarDigital Library
- AA Gurav and Manisha J Nene. 2020. Weakly Supervised Learning-based Table Detection. SN Computer Science 1(2020), 1–9.Google ScholarDigital Library
- Mrinal Haloi, Shashank Shekhar, Nikhil Fande, Siddhant Swaroop Dash, et al. 2022. Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method. arXiv preprint arXiv:2209.09207(2022).Google Scholar
- Mohamed A Hamada, Abdelrahman Abdallah, Mahmoud Kasem, and Mohamed Abokhalil. 2021. Neural Network Estimation Model to Optimize Timing and Schedule of Software Projects. In 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST). IEEE, 1–7.Google ScholarCross Ref
- Leipeng Hao, Liangcai Gao, Xiaohan Yi, and Zhi Tang. 2016. A table detection method for pdf documents based on convolutional neural networks. In 2016 12th IAPR Workshop on Document Analysis Systems (DAS). IEEE, 287–292.Google ScholarCross Ref
- Gaurav Harit and Anukriti Bansal. 2012. Table detection in document images using header and trailer patterns. In Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing. 1–8.Google Scholar
- Adam W Harley, Alex Ufkes, and Konstantinos G Derpanis. 2015. Evaluation of deep convolutional nets for document image classification and retrieval. In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 991–995.Google ScholarDigital Library
- Khurram Azeem Hashmi, Didier Stricker, Marcus Liwicki, Muhammad Noman Afzal, and Muhammad Zeshan Afzal. 2021. Guided table structure recognition through anchor optimization. IEEE Access 9(2021), 113521–113534.Google ScholarCross Ref
- Tamir Hassan and Robert Baumgartner. 2007. Table recognition and understanding from pdf files. In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), Vol. 2. IEEE, 1143–1147.Google Scholar
- Dafang He, Scott Cohen, Brian Price, Daniel Kifer, and C Lee Giles. 2017. Multi-scale multi-task fcn for semantic page segmentation and table detection. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Vol. 1. IEEE, 254–261.Google ScholarCross Ref
- Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick. 2017. Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV) (Oct 2017).Google Scholar
- Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Martin Eisenschlos. 2020. TaPas: Weakly supervised table parsing via pre-training. arXiv preprint arXiv:2004.02349(2020).Google Scholar
- Martin Holeček, Antonín Hoskovec, Petr Baudiš, and Pavel Klinger. 2019. Table understanding in structured documents. In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), Vol. 5. IEEE, 158–164.Google Scholar
- Jianying Hu, Ramanujan S Kashi, Daniel Lopresti, and Gordon T Wilfong. 2002. Evaluating the performance of table processing algorithms. International Journal on Document Analysis and Recognition 4, 3(2002), 140–153.Google ScholarCross Ref
- Yuan-Ting Hu, Jia-Bin Huang, and Alexander Schwing. 2017. Maskrnn: Instance level video object segmentation. Advances in neural information processing systems 30 (2017).Google Scholar
- Zilong Hu, Jinshan Tang, Ziming Wang, Kai Zhang, Ling Zhang, and Qingling Sun. 2018. Deep learning for image-based cancer detection and diagnosis- A survey. Pattern Recognition 83(2018), 134–149.Google ScholarDigital Library
- Yilun Huang, Qinqin Yan, Yibo Li, Yifan Chen, Xiong Wang, Liangcai Gao, and Zhi Tang. 2019. A YOLO-based table detection method. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 813–818.Google ScholarCross Ref
- Katsuhiko Itonori. 1993. Table structure recognition based on textblock arrangement and ruled line position. In Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93). IEEE, 765–768.Google ScholarCross Ref
- MAC Akmal Jahan and Roshan G Ragel. 2014. Locating tables in scanned documents for reconstructing and republishing. In 7th International Conference on Information and Automation for Sustainability. IEEE, 1–6.Google ScholarCross Ref
- Arushi Jain, Shubham Paliwal, Monika Sharma, and Lovekesh Vig. 2022. TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words. arXiv preprint arXiv:2203.06873(2022).Google Scholar
- K Jain, Anoop M Namboodiri, and Jayashree Subrahmonia. 2001. Structure in on-line documents. In Proceedings of Sixth International Conference on Document Analysis and Recognition. IEEE, 844–848.Google ScholarCross Ref
- Ertugrul Kara, Mark Traquair, Murat Simsek, Burak Kantarci, and Shahzad Khan. 2020. Holistic design for deep learning-based discovery of tabular structures in datasheet images. Engineering Applications of Artificial Intelligence 90 (2020), 103551.Google ScholarDigital Library
- Thotreingam Kasar, Philippine Barlas, Sebastien Adam, Clément Chatelain, and Thierry Paquet. 2013. Learning to detect tables in scanned document images using line information. In 2013 12th International Conference on Document Analysis and Recognition. IEEE, 1185–1189.Google ScholarDigital Library
- Mahmoud SalahEldin Kasem, Mohamed Hamada, and Islam Taj-Eddin. 2023. Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing. arXiv preprint arXiv:2302.01786(2023).Google Scholar
- Mahmoud SalahEldin Kasem, Mohamed Mahmoud, and Hyun-Soo Kang. 2023. Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey. arXiv preprint arXiv:2312.11812(2023).Google Scholar
- Isaak Kavasidis, Carmelo Pino, Simone Palazzo, Francesco Rundo, Daniela Giordano, P Messina, and Concetto Spampinato. 2019. A saliency-based convolutional neural network for table and chart detection in digitized documents. In International conference on image analysis and processing. Springer, 292–302.Google ScholarDigital Library
- Saqib Ali Khan, Syed Muhammad Daniyal Khalid, Muhammad Ali Shahzad, and Faisal Shafait. 2019. Table structure extraction with bi-directional gated recurrent unit networks. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 1366–1371.Google Scholar
- Shah Khusro, Asima Latif, and Irfan Ullah. 2015. On methods and tools of table detection, extraction and annotation in PDF documents. Journal of Information Science 41, 1 (2015), 41–57.Google ScholarDigital Library
- Thomas Kieninger and Andreas Dengel. 1998. The t-recs table recognition and analysis system. In International Workshop on Document Analysis Systems. Springer, 255–270.Google Scholar
- Yeon-Seok Kim and Kyong-Ho Lee. 2008. Extracting logical structures from HTML tables. Computer Standards & Interfaces 30, 5 (2008), 296–308.Google ScholarDigital Library
- Stefan Klampfl, Kris Jack, and Roman Kern. 2014. A comparison of two unsupervised table recognition methods from digital scientific articles. D-Lib Magazine 20, 11 (2014), 7.Google ScholarCross Ref
- Elvis Koci, Maik Thiele, Wolfgang Lehner, and Oscar Romero. 2018. Table recognition in spreadsheets via a graph representation. In 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 139–144.Google Scholar
- Elvis Koci, Maik Thiele, Josephine Rehak, Oscar Romero, and Wolfgang Lehner. 2019. DECO: A dataset of annotated spreadsheets for layout and table recognition. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 1280–1285.Google ScholarCross Ref
- Elvis Koci, Maik Thiele, Oscar Romero, and Wolfgang Lehner. 2019. A genetic-based search for adaptive table recognition in spreadsheets. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 1274–1279.Google ScholarCross Ref
- Tarun Kumar and Himanshu Sharad Bhatt. 2022. Evaluating Table Structure Recognition: A New Perspective. arXiv preprint arXiv:2208.00385(2022).Google Scholar
- Yann LeCun, Yoshua Bengio, Geoffrey Hinton, et al. 2015. Deep learning. nature, 521 (7553), 436-444. Google Scholar Google Scholar Cross Ref Cross Ref (2015).Google Scholar
- Benjamin Charles Germain Lee. 2017. Line detection in binary document scans: a case study with the International Tracing Service archives. In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2256–2261.Google Scholar
- Huichao Li, Lingze Zeng, Weiyu Zhang, Jianing Zhang, Ju Fan, and Meihui Zhang. 2022. A Two-Phase Approach for Recognizing Tables with Complex Structures. In International Conference on Database Systems for Advanced Applications. Springer, 587–595.Google Scholar
- Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, and Furu Wei. 2022. DiT: Self-supervised Pre-training for Document Image Transformer. arXiv preprint arXiv:2203.02378(2022).Google Scholar
- Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, and Zhoujun Li. 2020. Tablebank: Table benchmark for image-based table detection and recognition. In Proceedings of the 12th Language Resources and Evaluation Conference. 1918–1925.Google Scholar
- Shun Li, WeiDong Liu, and GongBing Xiao. 2019. Detection of Srew Nut Images Based on Deep Transfer Learning Network. In 2019 Chinese Automation Congress (CAC). IEEE, 951–955.Google Scholar
- Yibo Li, Liangcai Gao, Zhi Tang, Qinqin Yan, and Yilun Huang. 2019. A GAN-based feature generator for table detection. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 763–768.Google ScholarCross Ref
- Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez. 2017. A survey on deep learning in medical image analysis. Medical image analysis 42 (2017), 60–88.Google Scholar
- Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, and Matti Pietikäinen. 2020. Deep learning for generic object detection: A survey. International journal of computer vision 128, 2 (2020), 261–318.Google ScholarDigital Library
- Ruixue Liu, Shaozu Yuan, Aijun Dai, Lei Shen, Tiangang Zhu, Meng Chen, and Xiaodong He. 2022. Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline. In Proceedings of the 29th International Conference on Computational Linguistics. 3741–3752.Google Scholar
- Rujiao Long, Wen Wang, Nan Xue, Feiyu Gao, Zhibo Yang, Yongpan Wang, and Gui-Song Xia. 2021. Parsing table structures in the wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 944–952.Google ScholarCross Ref
- Nam Tuan Ly, Atsuhiro Takasu, Phuc Nguyen, and Hideaki Takeda. 2023. Rethinking Image-based Table Recognition Using Weakly Supervised Methods. arXiv preprint arXiv:2303.07641(2023).Google Scholar
- Chixiang Ma, Weihong Lin, Lei Sun, and Qiang Huo. 2023. Robust table detection and structure recognition from heterogeneous document images. Pattern Recognition 133(2023), 109006.Google ScholarDigital Library
- Mohamed Mahmoud and Hyun-Soo Kang. 2023. GANMasker: A Two-Stage Generative Adversarial Network for High-Quality Face Mask Removal. Sensors 23, 16 (2023), 7094.Google ScholarCross Ref
- Mohamed Mahmoud, Mahmoud Kasem, Abdelrahman Abdallah, and Hyun Soo Kang. 2022. AE-LSTM: Autoencoder with LSTM-Based Intrusion Detection in IoT. In 2022 International Telecommunications Conference (ITC-Egypt). IEEE, 1–6.Google Scholar
- Sabri A Mahmoud, Irfan Ahmad, Wasfi G Al-Khatib, Mohammad Alshayeb, Mohammad Tanvir Parvez, Volker Märgner, and Gernot A Fink. 2014. KHATT: An open Arabic offline handwritten text database. Pattern Recognition 47, 3 (2014), 1096–1112.Google ScholarDigital Library
- Song Mao, Azriel Rosenfeld, and Tapas Kanungo. 2003. Document structure analysis algorithms: a literature survey. Document recognition and retrieval X 5010 (2003), 197–207.Google Scholar
- Katleho L Masita, Ali N Hasan, and Satyakama Paul. 2018. Pedestrian detection using R-CNN object detector. In 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE, 1–6.Google ScholarCross Ref
- Shervin Minaee and Zhu Liu. 2017. Automatic question-answering using a deep similarity neural network. In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 923–927.Google ScholarCross Ref
- Ajoy Mondal, Peter Lipps, and CV Jawahar. 2020. IIIT-AR-13K: a new dataset for graphical object detection in documents. In International Workshop on Document Analysis Systems. Springer, 216–230.Google ScholarCross Ref
- Marcin Namysl, Alexander M Esser, Sven Behnke, and Joachim Köhler. 2022. Flexible Table Recognition and Semantic Interpretation System.. In VISIGRAPP (4: VISAPP). 27–37.Google Scholar
- Marcin Namysł, Alexander M Esser, Sven Behnke, and Joachim Köhler. 2023. Flexible Hybrid Table Recognition and Semantic Interpretation System. SN Computer Science 4, 3 (2023), 246.Google ScholarDigital Library
- Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, and Peter Staar. 2022. TableFormer: Table Structure Understanding with Transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4614–4623.Google ScholarCross Ref
- Duc-Dung Nguyen. 2022. TableSegNet: a fully convolutional network for table detection and segmentation in document images. International Journal on Document Analysis and Recognition (IJDAR) 25, 1(2022), 1–14.Google ScholarDigital Library
- Anssi Nurminen. 2013. Algorithmic extraction of data in tables in PDF documents. Master’s thesis.Google Scholar
- Daniyar Nurseitov, Kairat Bostanbekov, Daniyar Kurmankhojayev, Anel Alimova, Abdelrahman Abdallah, and Rassul Tolegenov. 2021. Handwritten Kazakh and Russian (HKR) database for text recognition. Multimedia Tools and Applications 80, 21 (2021), 33075–33097.Google ScholarDigital Library
- Lawrence O’Gorman. 1993. The document spectrum for page layout analysis. IEEE Transactions on pattern analysis and machine intelligence 15, 11(1993), 1162–1173.Google ScholarDigital Library
- Ermelinda Oro and Massimo Ruffolo. 2009. TREX: An approach for recognizing and extracting tables from PDF documents. In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 906–910.Google ScholarDigital Library
- Shubham Singh Paliwal, D Vishwanath, Rohit Rahul, Monika Sharma, and Lovekesh Vig. 2019. Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 128–133.Google ScholarCross Ref
- Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 311–318.Google Scholar
- Ihsin Tsaiyun Phillips. 1996. User’s reference manual for the UW english/technical document image database III. UW-III English/technical document image database manual (1996).Google Scholar
- Devashish Prasad, Ayan Gadpal, Kshitij Kapadni, Manish Visave, and Kavita Sultanpure. 2020. 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. 572–573.Google ScholarCross Ref
- P Pyreddy and WB Croft. 1997. Tinti: A system for retrieval in text tables title2.Google ScholarDigital Library
- Shah Rukh Qasim, Hassan Mahmood, and Faisal Shafait. 2019. Rethinking table recognition using graph neural networks. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 142–147.Google ScholarCross Ref
- Liang Qiao, Zaisheng Li, Zhanzhan Cheng, Peng Zhang, Shiliang Pu, Yi Niu, Wenqi Ren, Wenming Tan, and Fei Wu. 2021. Lgpma: Complicated table structure recognition with local and global pyramid mask alignment. In International conference on document analysis and recognition. Springer, 99–114.Google ScholarDigital Library
- Sachin Raja, Ajoy Mondal, and CV Jawahar. 2020. Table structure recognition using top-down and bottom-up cues. In European Conference on Computer Vision. Springer, 70–86.Google Scholar
- Sachin Raja, Ajoy Mondal, and CV Jawahar. 2022. Visual Understanding of Complex Table Structures from Document Images. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2299–2308.Google Scholar
- Susie Xi Rao12, Johannes Rausch, Peter Egger, and Ce Zhang. 2021. TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets. (2021).Google Scholar
- Sheikh Faisal Rashid, Abdullah Akmal, Muhammad Adnan, Ali Adnan Aslam, and Andreas Dengel. 2017. Table recognition in heterogeneous documents using machine learning. In 2017 14th IAPR International conference on document analysis and recognition (ICDAR), Vol. 1. IEEE, 777–782.Google Scholar
- Mohammad Mohsin Reza, Syed Saqib Bukhari, Martin Jenckel, and Andreas Dengel. 2019. Table localization and segmentation using gan and cnn. In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), Vol. 5. IEEE, 152–157.Google Scholar
- Pau Riba, Anjan Dutta, Lutz Goldmann, Alicia Fornés, Oriol Ramos, and Josep Lladós. 2019. Table detection in invoice documents by graph neural networks. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 122–127.Google Scholar
- Pau Riba, Lutz Goldmann, Oriol Ramos Terrades, Diede Rusticus, Alicia Fornés, and Josep Lladós. 2022. Table detection in business document images by message passing networks. Pattern Recognition 127(2022), 108641.Google ScholarDigital Library
- Arash Samari, Andrew Piper, Alison Hedley, and Mohamed Cheriet. 2021. Weakly supervised bounding box extraction for unlabeled data in table detection. In Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10-15, 2021, Proceedings, Part VII. Springer, 339–352.Google ScholarDigital Library
- Sebastian Schreiber, Stefan Agne, Ivo Wolf, Andreas Dengel, and Sheraz Ahmed. 2017. 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. IEEE, 1162–1167.Google ScholarCross Ref
- Wonkyo Seo, Hyung Il Koo, and Nam Ik Cho. 2015. Junction-based table detection in camera-captured document images. International Journal on Document Analysis and Recognition (IJDAR) 18, 1(2015), 47–57.Google ScholarDigital Library
- Faisal Shafait and Ray Smith. 2010. Table detection in heterogeneous documents. In Proceedings of the 9th IAPR International Workshop on Document Analysis Systems. 65–72.Google ScholarDigital Library
- Asif Shahab, Faisal Shafait, Thomas Kieninger, and Andreas Dengel. 2010. An open approach towards the benchmarking of table structure recognition systems. In Proceedings of the 9th IAPR International Workshop on Document Analysis Systems. 113–120.Google ScholarDigital Library
- Tahira Shehzadi, Khurram Azeem Hashmi, Didier Stricker, Marcus Liwicki, and Muhammad Zeshan Afzal. 2023. Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer. In International Conference on Document Analysis and Recognition. Springer, 51–76.Google Scholar
- Xinyi Shen, Lingjun Kong, Yunchao Bao, Yaowei Zhou, and Weiguang Liu. 2022. RCANet: A Rows and Columns Aggregated Network for Table Structure Recognition. In 2022 3rd Information Communication Technologies Conference (ICTC). IEEE, 112–116.Google Scholar
- Shoaib Ahmed Siddiqui, Imran Ali Fateh, Syed Tahseen Raza Rizvi, Andreas Dengel, and Sheraz Ahmed. 2019. DeepTabStR: deep learning based table structure recognition. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 1403–1409.Google ScholarCross Ref
- Shoaib Ahmed Siddiqui, Pervaiz Iqbal Khan, Andreas Dengel, and Sheraz Ahmed. 2019. Rethinking semantic segmentation for table structure recognition in documents. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 1397–1402.Google ScholarCross Ref
- Shoaib Ahmed Siddiqui, Muhammad Imran Malik, Stefan Agne, Andreas Dengel, and Sheraz Ahmed. 2018. Decnt: Deep deformable cnn for table detection. IEEE access 6(2018), 74151–74161.Google Scholar
- Grigori Sidorov, Helena Gómez-Adorno, Ilia Markov, David Pinto, and Nahun Loya. 2015. Computing text similarity using Tree Edit Distance. In 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC). 1–4. https://doi.org/10.1109/NAFIPS-WConSC.2015.7284129Google ScholarCross Ref
- Noah Siegel, Nicholas Lourie, Russell Power, and Waleed Ammar. 2018. Extracting scientific figures with distantly supervised neural networks. In Proceedings of the 18th ACM/IEEE on joint conference on digital libraries. 223–232.Google ScholarDigital Library
- Brandon Smock, Rohith Pesala, and Robin Abraham. 2023. GriTS: Grid table similarity metric for table structure recognition. In International Conference on Document Analysis and Recognition. Springer, 535–549.Google ScholarDigital Library
- Brandon Smock, Rohith Pesala, Robin Abraham, and WA Redmond. 2021. PubTables-1M: Towards comprehensive table extraction from unstructured documents. arXiv preprint arXiv:2110.00061(2021).Google Scholar
- Ningning Sun, Yuanping Zhu, and Xiaoming Hu. 2019. Faster R-CNN based table detection combining corner locating. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 1314–1319.Google ScholarCross Ref
- Richard Szeliski. 2010. Computer vision: algorithms and applications. Springer Science & Business Media.Google ScholarDigital Library
- Chris Tensmeyer, Vlad I Morariu, Brian Price, Scott Cohen, and Tony Martinez. 2019. Deep splitting and merging for table structure decomposition. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 114–121.Google ScholarCross Ref
- Nazgul Toiganbayeva, Mahmoud Kasem, Galymzhan Abdimanap, Kairat Bostanbekov, Abdelrahman Abdallah, Anel Alimova, and Daniyar Nurseitov. 2022. Kohtd: Kazakh offline handwritten text dataset. Signal Processing: Image Communication 108 (2022), 116827.Google ScholarDigital Library
- Mark Traquair, Ertugrul Kara, Burak Kantarci, and Shahzad Khan. 2019. Deep learning for the detection of tabular information from electronic component datasheets. In 2019 IEEE Symposium on Computers and Communications (ISCC). IEEE, 1–6.Google ScholarCross Ref
- Scott Tupaj, Zhongwen Shi, C Hwa Chang, and Hassan Alam. 1996. Extracting tabular information from text files. EECS Department, Tufts University, Medford, USA 1 (1996).Google Scholar
- Yalin Wang and Jianying Hu. 2002. A machine learning based approach for table detection on the web. In Proceedings of the 11th international conference on World Wide Web. 242–250.Google ScholarDigital Library
- Yalin Wangt, Ihsin T Phillipst, and Robert Haralick. 2001. Automatic table ground truth generation and a background-analysis-based table structure extraction method. In Proceedings of Sixth International Conference on Document Analysis and Recognition. IEEE, 528–532.Google Scholar
- Shengkai Wu, Jinrong Yang, Xinggang Wang, and Xiaoping Li. 2019. Iou-balanced loss functions for single-stage object detection. arXiv preprint arXiv:1908.05641(2019).Google Scholar
- Bin Xiao, Murat Simsek, Burak Kantarci, and Ala Abu Alkheir. 2022. Table Structure Recognition with Conditional Attention. arXiv preprint arXiv:2203.03819(2022).Google Scholar
- Bin Xiao, Murat Simsek, Burak Kantarci, and Ala Abu Alkheir. 2023. Revisiting Table Detection Datasets for Visually Rich Documents. arXiv preprint arXiv:2305.04833(2023).Google Scholar
- Wen Xu, Julian Jang-Jaccard, Amardeep Singh, Yuanyuan Wei, and Fariza Sabrina. 2021. Improving performance of autoencoder-based network anomaly detection on nsl-kdd dataset. IEEE Access 9(2021), 140136–140146.Google ScholarCross Ref
- Wenyuan Xue, Qingyong Li, and Dacheng Tao. 2019. ReS2TIM: Reconstruct syntactic structures from table images. In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 749–755.Google ScholarCross Ref
- Fan Yang, Lei Hu, Xinwu Liu, Shuangping Huang, and Zhenghui Gu. 2023. A large-scale dataset for end-to-end table recognition in the wild. Scientific Data 10, 1 (2023), 110.Google ScholarCross Ref
- Jing Yang and Guanci Yang. 2018. Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer. Algorithms 11, 3 (2018), 28.Google ScholarCross Ref
- Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018. Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine 13, 3 (2018), 55–75.Google Scholar
- Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang. 2019. Free-form image inpainting with gated convolution. In Proceedings of the IEEE/CVF international conference on computer vision. 4471–4480.Google ScholarCross Ref
- Richard Zanibbi, Dorothea Blostein, and James R Cordy. 2004. A survey of table recognition. Document Analysis and Recognition 7, 1 (2004), 1–16.Google ScholarDigital Library
- Daqian Zhang, Ruibin Mao, Runting Guo, Yang Jiang, and Jing Zhu. 2022. YOLO-table: disclosure document table detection with involution. International Journal on Document Analysis and Recognition (IJDAR) (2022), 1–14.Google Scholar
- Xi-wen Zhang, Michael R Lyu, and Guo-zhong Dai. 2007. Extraction and segmentation of tables from Chinese ink documents based on a matrix model. Pattern recognition 40, 7 (2007), 1855–1867.Google Scholar
- Zixing Zhang, Jürgen Geiger, Jouni Pohjalainen, Amr El-Desoky Mousa, Wenyu Jin, and Björn Schuller. 2018. Deep learning for environmentally robust speech recognition: An overview of recent developments. ACM Transactions on Intelligent Systems and Technology (TIST) 9, 5(2018), 1–28.Google ScholarDigital Library
- Zhenrong Zhang, Jianshu Zhang, Jun Du, and Fengren Wang. 2022. Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126(2022), 108565.Google ScholarDigital Library
- Xinyi Zheng, Doug Burdick, Lucian Popa, Peter Zhong, and Nancy Xin Ru Wang. 2021. Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context. Winter Conference for Applications in Computer Vision (WACV) (2021).Google Scholar
- Xinyi Zheng, Douglas Burdick, Lucian Popa, Xu Zhong, and Nancy Xin Ru Wang. 2021. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In Proceedings of the IEEE/CVF winter conference on applications of computer vision. 697–706.Google ScholarCross Ref
- Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. 2020. Image-based table recognition: data, model, and evaluation. In European Conference on Computer Vision. Springer, 564–580.Google ScholarDigital Library
- Yajun Zou and Jinwen Ma. 2020. A deep semantic segmentation model for image-based table structure recognition. In 2020 15th IEEE International Conference on Signal Processing (ICSP), Vol. 1. IEEE, 274–280.Google ScholarCross Ref
- Arthur Zucker, Younes Belkada, Hanh Vu, and Van Nam Nguyen. 2021. ClusTi: Clustering method for table structure recognition in scanned images. Mobile Networks and Applications 26, 4 (2021), 1765–1776.Google ScholarDigital Library
Index Terms
- Deep Learning for Table Detection and Structure Recognition: A Survey
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