Deep learning for table detection and structure recognition: A survey.

Table Detection and Structure Recognition

This is the repository for the collection of Table Detection and Structure Recognition models and Datasets.

If you find this repository helpful, you may consider cite our relevant work:

• Kasem, Mahmoud, Abdelrahman Abdallah, Alexander Berendeyev, Ebrahem Elkady, Mahmoud Abdalla, Mohamed Mahmoud, Mohamed Hamada, Daniyar Nurseitov and Islam Taj-Eddin. “Deep learning for table detection and structure recognition: A survey.” (2022). Link
• Abdallah, Abdelrahman, Alexander Berendeyev, Islam Nuradin, and Daniyar Nurseitov. "TNCR: Table net detection and classification dataset." Neurocomputing 473 (2022): 79-97. Link

Relevant Repositories

• TNCR: Table Net Detection and Classification Dataset Link.

Table detection Datasets

• ICDAR2013 dataset: Göbel, Max, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. "ICDAR 2013 table competition." In 2013 12th International Conference on Document Analysis and Recognition, pp. 1449-1453. IEEE, 2013 Paper Link, Home Page Link.

• ICDAR 2017 POD: Gao, Liangcai, Xiaohan Yi, Zhuoren Jiang, Leipeng Hao, and Zhi Tang. "ICDAR2017 competition on page object detection." In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1417-1422. IEEE, 2017. Paper Link, Home Page Link.

• ICDAR2019 : Gao, Liangcai, Yilun Huang, Hervé Déjean, Jean-Luc Meunier, Qinqin Yan, Yu Fang, Florian Kleber, and Eva Lang. "ICDAR 2019 competition on table detection and recognition (cTDaR)." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1510-1515. IEEE, 2019. Paper Link, Home Page Link.

• TabStructDB : Siddiqui, Shoaib Ahmed, Imran Ali Fateh, Syed Tahseen Raza Rizvi, Andreas Dengel, and Sheraz Ahmed. "Deeptabstr: Deep learning based table structure recognition." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1403-1409. IEEE, 2019. Paper Link, Home Page Link .

• TABLE2LATEX-450K : Deng, Yuntian, David Rosenberg, and Gideon Mann. "Challenges in end-to-end neural scientific table recognition." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 894-901. IEEE, 2019. Paper Link, Home Page Link.

• RVL-CDIP (SUBSET) : Harley, Adam W., Alex Ufkes, and Konstantinos G. Derpanis. "Evaluation of deep convolutional nets for document image classification and retrieval." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 991-995. IEEE, 2015. Paper Link, Home Page Link.

• IIIT-AR-13K : Mondal, Ajoy, Peter Lipps, and C. V. Jawahar. "IIIT-AR-13K: a new dataset for graphical object detection in documents." In International Workshop on Document Analysis Systems, pp. 216-230. Springer, Cham, 2020. Paper Link, Home Page Link.

• CamCap : Seo, Wonkyo, Hyung Il Koo, and Nam Ik Cho. "Junction-based table detection in camera-captured document images." International Journal on Document Analysis and Recognition (IJDAR) 18, no. 1 (2015): 47-57. Paper Link, Home Page Link.

• UNLV Table : Shahab, Asif, Faisal Shafait, Thomas Kieninger, and Andreas Dengel. "An open approach towards the benchmarking of table structure recognition systems." In Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, pp. 113-120. 2010. Paper Link, Home Page Link.

• UW-3 Table : Phillips, Ihsin Tsaiyun. "User’s reference manual for the UW english/technical document image database III." UW-III English/technical document image database manual (1996). Paper Link, Home Page Link.

• Marmot : Fang, Jing, Xin Tao, Zhi Tang, Ruiheng Qiu, and Ying Liu. "Dataset, ground-truth and performance metrics for table detection evaluation." In 2012 10th IAPR International Workshop on Document Analysis Systems, pp. 445-449. IEEE, 2012. Paper Link, Home Page Link.

• TableBank : Li, Minghao, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, and Zhoujun Li. "Tablebank: Table benchmark for image-based table detection and recognition." In Proceedings of The 12th language resources and evaluation conference, pp. 1918-1925. 2020. Paper Link, Home Page Link.

• DeepFigures : Siegel, Noah, Nicholas Lourie, Russell Power, and Waleed Ammar. "Extracting scientific figures with distantly supervised neural networks." In Proceedings of the 18th ACM/IEEE on joint conference on digital libraries, pp. 223-232. 2018. Paper Link, Home Page Link.

• PubTables-1M : Smock, Brandon, Rohith Pesala, and Robin Abraham. "PubTables-1M: Towards comprehensive table extraction from unstructured documents." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4634-4642. 2022. Paper Link, Home Page Link.

• SciTSR : Chi, Zewen, Heyan Huang, Heng-Da Xu, Houjin Yu, Wanxuan Yin, and Xian-Ling Mao. "Complicated table structure recognition." arXiv preprint arXiv:1908.04729 (2019). Paper Link, Home Page Link.

• FinTabNet : Zheng, Xinyi, Douglas Burdick, Lucian Popa, Xu Zhong, and Nancy Xin Ru Wang. "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, pp. 697-706. 2021. Paper Link, Home Page Link.

• PubTabNet : Zhong, Xu, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. "Image-based table recognition: data, model, and evaluation." In European Conference on Computer Vision, pp. 564-580. Springer, Cham, 2020. Paper Link, Home Page Link.

• TNCR : Abdallah, Abdelrahman, Alexander Berendeyev, Islam Nuradin, and Daniyar Nurseitov. "TNCR: Table net detection and classification dataset." Neurocomputing 473 (2022): 79-97. Paper Link, Home Page Link.

• SynthTabNet :Nassar, Ahmed, Nikolaos Livathinos, Maksym Lysak, and Peter Staar. "TableFormer: Table Structure Understanding with Transformers." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4614-4623. 2022. Paper Link, Home Page Link.

• CTE : Gemelli, A., Vivoli, E., & Marinai, S. (2023). CTE: A Dataset for Contextualized Table Extraction. arXiv preprint arXiv:2302.01451. Paper Link, Home Page Link

Table Detection and Structure Recognition papers

1993

Journal

• O'Gorman, Lawrence. "The document spectrum for page layout analysis." IEEE Transactions on pattern analysis and machine intelligence 15, no. 11 (1993): 1162-1173. Paper Link

Conference

• Itonori, Katsuhiko. "Table structure recognition based on textblock arrangement and ruled line position." In Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR'93), pp. 765-768. IEEE, 1993. Paper Link
• Chandran, Surekha, and Rangachar Kasturi. "Structural recognition of tabulated data." In Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR'93), pp. 516-519. IEEE, 1993. Paper Link

1997

Conference

• Pyreddy, P., and W. B. Croft. "Tinti: A system for retrieval in text tables title2." (1997). Paper Link

1998

Conference

Kieninger, Thomas, and Andreas Dengel. "The t-recs table recognition and analysis system." In International Workshop on Document Analysis Systems, pp. 255-270. Springer, Berlin, Heidelberg, 1998. Paper Link

2001

Conference

• Wangt, Yalin, Ihsin T. Phillipst, and Robert Haralick. "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, pp. 528-532. IEEE, 2001. Paper Link

2002

Conference

• Cesarini, Francesca, Simone Marinai, L. Sarti, and Giovanni Soda. "Trainable table location in document images." In 2002 International Conference on Pattern Recognition, vol. 3, pp. 236-240. IEEE, 2002.Paper Link
• Wang, Yalin, and Jianying Hu. "A machine learning based approach for table detection on the web." In Proceedings of the 11th international conference on World Wide Web, pp. 242-250. 2002. Paper Link

2007

Conference

• Hassan, Tamir, and Robert Baumgartner. "Table recognition and understanding from pdf files." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), vol. 2, pp. 1143-1147. IEEE, 2007. Paper Link

2009

Conference

• Oro, Ermelinda, and Massimo Ruffolo. "TREX: An approach for recognizing and extracting tables from PDF documents." In 2009 10th International Conference on Document Analysis and Recognition, pp. 906-910. IEEE, 2009. Paper Link
• e Silva, Ana Costa. "Learning rich hidden markov models in document analysis: Table location." In 2009 10th International Conference on Document Analysis and Recognition, pp. 843-847. IEEE, 2009.Paper Link

2010

Conference

• Shafait, Faisal, and Ray Smith. "Table detection in heterogeneous documents." In Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, pp. 65-72. 2010. Paper Link

2012

Conference

• Fang, Jing, Prasenjit Mitra, Zhi Tang, and C. Lee Giles. "Table header detection and classification." In Twenty-Sixth AAAI Conference on Artificial Intelligence. 2012. Paper Link

2013

Conference

• Nurminen, Anssi. "Algorithmic extraction of data in tables in PDF documents." Master's thesis, 2013. Paper Link
• Kasar, Thotreingam, Philippine Barlas, Sebastien Adam, Clément Chatelain, and Thierry Paquet. "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. Paper Link

2014

Conference

• Jahan, MAC Akmal, and Roshan G. Ragel. "Locating tables in scanned documents for reconstructing and republishing." In 7th International Conference on Information and Automation for Sustainability, pp. 1-6. IEEE, 2014.Paper Link
• Klampfl, Stefan, Kris Jack, and Roman Kern. "A comparison of two unsupervised table recognition methods from digital scientific articles." D-Lib Magazine 20, no. 11 (2014): 7. Paper Link

2015

Journal

• Seo, Wonkyo, Hyung Il Koo, and Nam Ik Cho. "Junction-based table detection in camera-captured document images." International Journal on Document Analysis and Recognition (IJDAR) 18, no. 1 (2015): 47-57.Paper Link

Preprint

• Fan, Miao, and Doo Soon Kim. "Table region detection on large-scale PDF files without labeled data." CoRR, abs/1506.08891 (2015). Paper Link

2016

Conference

• Hao, Leipeng, Liangcai Gao, Xiaohan Yi, and Zhi Tang. "A table detection method for pdf documents based on convolutional neural networks." In 2016 12th IAPR Workshop on Document Analysis Systems (DAS), pp. 287-292. IEEE, 2016. Paper Link
• Hao, Leipeng, Liangcai Gao, Xiaohan Yi, and Zhi Tang. "A table detection method for pdf documents based on convolutional neural networks." In 2016 12th IAPR Workshop on Document Analysis Systems (DAS), pp. 287-292. IEEE, 2016. Paper Link

2017

Conference

• Gilani, Azka, Shah Rukh Qasim, Imran Malik, and Faisal Shafait. "Table detection using deep learning." In 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol. 1, pp. 771-776. IEEE, 2017.Paper Link
• Schreiber, Sebastian, Stefan Agne, Ivo Wolf, Andreas Dengel, and Sheraz Ahmed. "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. Paper Link
• He, Dafang, Scott Cohen, Brian Price, Daniel Kifer, and C. Lee Giles. "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, pp. 254-261. IEEE, 2017. Paper Link
• Gilani, Azka, Shah Rukh Qasim, Imran Malik, and Faisal Shafait. "Table detection using deep learning." In 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol. 1, pp. 771-776. IEEE, 2017. Paper Link
• Rashid, Sheikh Faisal, Abdullah Akmal, Muhammad Adnan, Ali Adnan Aslam, and Andreas Dengel. "Table recognition in heterogeneous documents using machine learning." In 2017 14th IAPR International conference on document analysis and recognition (ICDAR), vol. 1, pp. 777-782. IEEE, 2017. Paper Link

2018

Journal

• Siddiqui, Shoaib Ahmed, Muhammad Imran Malik, Stefan Agne, Andreas Dengel, and Sheraz Ahmed. "Decnt: Deep deformable cnn for table detection." IEEE access 6 (2018): 74151-74161. Paper Link

Conference

• Arif, Saman, and Faisal Shafait. "Table detection in document images using foreground and background features." In 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1-8. IEEE, 2018. Paper Link
• Koci, Elvis, Maik Thiele, Wolfgang Lehner, and Oscar Romero. "Table recognition in spreadsheets via a graph representation." In 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 139-144. IEEE, 2018. Paper Link

2019

Conference

• Reza, Mohammad Mohsin, Syed Saqib Bukhari, Martin Jenckel, and Andreas Dengel. "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. Paper Link
• Paliwal, Shubham Singh, D. Vishwanath, Rohit Rahul, Monika Sharma, and Lovekesh Vig. "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), pp. 128-133. IEEE, 2019. Paper Link
• Huang, Yilun, Qinqin Yan, Yibo Li, Yifan Chen, Xiong Wang, Liangcai Gao, and Zhi Tang. "A YOLO-based table detection method." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 813-818. IEEE, 2019. Paper Link
• Sun, Ningning, Yuanping Zhu, and Xiaoming Hu. "Faster R-CNN based table detection combining corner locating." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1314-1319. IEEE, 2019. Paper Link
• Kavasidis, Isaak, Carmelo Pino, Simone Palazzo, Francesco Rundo, Daniela Giordano, P. Messina, and Concetto Spampinato. "A saliency-based convolutional neural network for table and chart detection in digitized documents." In International conference on image analysis and processing, pp. 292-302. Springer, Cham, 2019. Paper Link
• Holeček, Martin, Antonín Hoskovec, Petr Baudiš, and Pavel Klinger. "Table understanding in structured documents." In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 5, pp. 158-164. IEEE, 2019. Paper Link
• Li, Yibo, Liangcai Gao, Zhi Tang, Qinqin Yan, and Yilun Huang. "A GAN-based feature generator for table detection." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 763-768. IEEE, 2019. Paper Link
• Riba, Pau, Anjan Dutta, Lutz Goldmann, Alicia Fornés, Oriol Ramos, and Josep Lladós. "Table detection in invoice documents by graph neural networks." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 122-127. IEEE, 2019. Paper Link
• Siddiqui, Shoaib Ahmed, Imran Ali Fateh, Syed Tahseen Raza Rizvi, Andreas Dengel, and Sheraz Ahmed. "Deeptabstr: Deep learning based table structure recognition." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1403-1409. IEEE, 2019. Paper Link
• Koci, Elvis, Maik Thiele, Oscar Romero, and Wolfgang Lehner. "A genetic-based search for adaptive table recognition in spreadsheets." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1274-1279. IEEE, 2019. Paper Link
• Siddiqui, Shoaib Ahmed, Pervaiz Iqbal Khan, Andreas Dengel, and Sheraz Ahmed. "Rethinking semantic segmentation for table structure recognition in documents." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1397-1402. IEEE, 2019. Paper Link
• Khan, Saqib Ali, Syed Muhammad Daniyal Khalid, Muhammad Ali Shahzad, and Faisal Shafait. "Table structure extraction with bi-directional gated recurrent unit networks." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1366-1371. IEEE, 2019. Paper Link
• Qasim, Shah Rukh, Hassan Mahmood, and Faisal Shafait. "Rethinking table recognition using graph neural networks." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 142-147. IEEE, 2019. Paper Link
• Deng, Yuntian, David Rosenberg, and Gideon Mann. "Challenges in end-to-end neural scientific table recognition." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 894-901. IEEE, 2019. Paper Link
• Xue, Wenyuan, Qingyong Li, and Dacheng Tao. "ReS2TIM: Reconstruct syntactic structures from table images." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 749-755. IEEE, 2019. Paper Link
• Tensmeyer, Chris, Vlad I. Morariu, Brian Price, Scott Cohen, and Tony Martinez. "Deep splitting and merging for table structure decomposition." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 114-121. IEEE, 2019. Paper Link
• Siddiqui, Shoaib Ahmed, Imran Ali Fateh, Syed Tahseen Raza Rizvi, Andreas Dengel, and Sheraz Ahmed. "Deeptabstr: Deep learning based table structure recognition." In 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1403-1409. IEEE, 2019. Paper Link

2020

Conference

• Carion, Nicolas, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. "End-to-end object detection with transformers." In European conference on computer vision, pp. 213-229. Springer, Cham, 2020. Paper Link
• Prasad, Devashish, Ayan Gadpal, Kshitij Kapadni, Manish Visave, and Kavita Sultanpure. "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. Paper Link
• Casado-García, Á., Domínguez, C., Heras, J., Mata, E. and Pascual, V., 2020, July. The benefits of close-domain fine-tuning for table detection in document images. In International workshop on document analysis systems (pp. 199-215). Springer, Cham. Paper Link
• Li, Minghao, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, and Zhoujun Li. "Tablebank: Table benchmark for image-based table detection and recognition." In Proceedings of The 12th language resources and evaluation conference, pp. 1918-1925. 2020. Paper Link
• Raja, Sachin, Ajoy Mondal, and C. V. Jawahar. "Table structure recognition using top-down and bottom-up cues." In European Conference on Computer Vision, pp. 70-86. Springer, Cham, 2020. Paper Link
• Zou, Yajun, and Jinwen Ma. "A deep semantic segmentation model for image-based table structure recognition." In 2020 15th IEEE International Conference on Signal Processing (ICSP), vol. 1, pp. 274-280. IEEE, 2020. Paper Link
• Zou, Yajun, and Jinwen Ma. "A deep semantic segmentation model for image-based table structure recognition." In 2020 15th IEEE International Conference on Signal Processing (ICSP), vol. 1, pp. 274-280. IEEE, 2020. Paper Link
• Zhong, Xu, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. "Image-based table recognition: data, model, and evaluation." In European Conference on Computer Vision, pp. 564-580. Springer, Cham, 2020. Paper Link

2021

Journal

• Zucker, Arthur, Younes Belkada, Hanh Vu, and Van Nam Nguyen. "ClusTi: Clustering method for table structure recognition in scanned images." Mobile Networks and Applications 26, no. 4 (2021): 1765-1776. Paper Link
• Hashmi, Khurram Azeem, Didier Stricker, Marcus Liwicki, Muhammad Noman Afzal, and Muhammad Zeshan Afzal. "Guided table structure recognition through anchor optimization." IEEE Access 9 (2021): 113521-113534. Paper Link
• Hashmi, Khurram Azeem, Alain Pagani, Marcus Liwicki, Didier Stricker, and Muhammad Zeshan Afzal. "CasTabDetectoRS: cascade network for table detection in document images with recursive feature pyramid and switchable atrous convolution." Journal of Imaging 7, no. 10 (2021): 214. Paper Link
• Nazir, Danish, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, and Muhammad Zeshan Afzal. "HybridTabNet: Towards better table detection in scanned document images." Applied Sciences 11, no. 18 (2021): 8396. Paper Link
• Hashmi, Khurram Azeem, Didier Stricker, Marcus Liwicki, Muhammad Noman Afzal, and Muhammad Zeshan Afzal. "Guided table structure recognition through anchor optimization." IEEE Access 9 (2021): 113521-113534. Paper Link
• Phan, Hai-Hong, and Ngo Dai Duong. "An Integrated Approach for Table Detection and Structure Recognition." Journal on Information Technologies & Communications 2021, no. 1 (2021): 41-50.Paper Link

Conference

• Agarwal, Madhav, Ajoy Mondal, and C. V. Jawahar. "Cdec-net: Composite deformable cascade network for table detection in document images." In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9491-9498. IEEE, 2021. * Paper Link
• Zheng, Xinyi, Douglas Burdick, Lucian Popa, Xu Zhong, and Nancy Xin Ru Wang. "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, pp. 697-706. 2021. Paper Link
• Xue, Wenyuan, Baosheng Yu, Wen Wang, Dacheng Tao, and Qingyong Li. "Tgrnet: A table graph reconstruction network for table structure recognition." In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1295-1304. 2021. Paper Link
• Mikhailov, Andrey, and Alexey Shigarov. "Page Layout Analysis for Refining Table Extraction from PDF Documents." In 2021 Ivannikov Ispras Open Conference (ISPRAS), pp. 114-119. IEEE, 2021. Paper Link
• Ziomek, Juliusz, and Stuart E. Middleton. "GloSAT Historical Measurement Table Dataset: Enhanced Table Structure Recognition Annotation for Downstream Historical Data Rescue." In The 6th International Workshop on Historical Document Imaging and Processing, pp. 49-54. 2021. Paper Link
• Li, Xiao-Hui, Fei Yin, Xu-Yao Zhang, and Cheng-Lin Liu. "Adaptive scaling for archival table structure recognition." In International Conference on Document Analysis and Recognition, pp. 80-95. Springer, Cham, 2021.Paper Link
• Qiao, Liang, Zaisheng Li, Zhanzhan Cheng, Peng Zhang, Shiliang Pu, Yi Niu, Wenqi Ren, Wenming Tan, and Fei Wu. "Lgpma: Complicated table structure recognition with local and global pyramid mask alignment." In International Conference on Document Analysis and Recognition, pp. 99-114. Springer, Cham, 2021. Paper Link
• Liu, Hao, Xin Li, Bing Liu, Deqiang Jiang, Yinsong Liu, Bo Ren, and Rongrong Ji. "Show, read and reason: Table structure recognition with flexible context aggregator." In Proceedings of the 29th ACM International Conference on Multimedia, pp. 1084-1092. 2021. Paper Link
• Li, Yibo, Yilun Huang, Ziyi Zhu, Lemeng Pan, Yongshuai Huang, Lin Du, Zhi Tang, and Liangcai Gao. "Rethinking table structure recognition using sequence labeling methods." In International Conference on Document Analysis and Recognition, pp. 541-553. Springer, Cham, 2021.Paper Link
• Kong, Lingjun, Yunchao Bao, Qianwen Wang, Lijun Cao, and Shengmei Zhao. "A gradient heatmap based table structure recognition." In 2021 13th International Conference on Machine Learning and Computing, pp. 456-463. 2021.Paper Link
• Khan, Umar, Sohaib Zahid, Muhammad Asad Ali, Adnan Ul-Hasan, and Faisal Shafait. "TabAug: data driven augmentation for enhanced table structure recognition." In International Conference on Document Analysis and Recognition, pp. 585-601. Springer, Cham, 2021.Paper Link
• Fischer, Pascal, Alen Smajic, Giuseppe Abrami, and Alexander Mehler. "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 German Conference on Artificial Intelligence (Künstliche Intelligenz), pp. 95-108. Springer, Cham, 2021. Paper Link
• Wei, Dafeng, Hongtao Lu, Yi Zhou, and Kai Chen. "Image-based Table Cell Detection: a Novel Table Structure Decomposition Method with New Dataset." In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 1-7. IEEE, 2021. Paper Link
• Pegu, Bhanupriya, Maneet Singh, Aakash Agarwal, Aniruddha Mitra, and Karamjit Singh. "Table Structure Recognition Using CoDec Encoder-Decoder." In International Conference on Document Analysis and Recognition, pp. 66-80. Springer, Cham, 2021. Paper Link
• Li, Yiren, Zheng Huang, Junchi Yan, Yi Zhou, Fan Ye, and Xianhui Liu. "GFTE: graph-based financial table extraction." In International Conference on Pattern Recognition, pp. 644-658. Springer, Cham, 2021. Paper Link
• Samari, Arash, Andrew Piper, Alison Hedley, and Mohamed Cheriet. "Weakly supervised bounding box extraction for unlabeled data in table detection." In International Conference on Pattern Recognition, pp. 339-352. Springer, Cham, 2021.Paper Link
• Ichikawa, Koji. "Image-Based Relation Classification Approach for Table Structure Recognition." In International Conference on Document Analysis and Recognition, pp. 632-647. Springer, Cham, 2021.Paper Link

Preprint

• Namysl, Marcin, Alexander M. Esser, Sven Behnke, and Joachim Köhler. "Tab. IAIS: Flexible Table Recognition and Semantic Interpretation System." arXiv preprint arXiv:2105.11879 (2021). Paper Link

2022

Journal

• Riba, Pau, Lutz Goldmann, Oriol Ramos Terrades, Diede Rusticus, Alicia Fornés, and Josep Lladós. "Table detection in business document images by message passing networks." Pattern Recognition 127 (2022): 108641. Paper Link

• Kwon, Hyebin, Joungbin An, Dongwoo Lee, and Won-Yong Shin. "DATa: Domain Adaptation-aided deep Table detection using visual–lexical representations." Knowledge-Based Systems (2022): 109946. Paper Link

• Nguyen, Duc-Dung. "TableSegNet: a fully convolutional network for table detection and segmentation in document images." International Journal on Document Analysis and Recognition (IJDAR) 25, no. 1 (2022): 1-14. Paper Link

• Nguyen, Duc-Dung. "TableSegNet: a fully convolutional network for table detection and segmentation in document images." International Journal on Document Analysis and Recognition (IJDAR) 25, no. 1 (2022): 1-14. Paper Link

• Zhang, Daqian, Ruibin Mao, Runting Guo, Yang Jiang, and Jing Zhu. "YOLO-table: disclosure document table detection with involution." International Journal on Document Analysis and Recognition (IJDAR) (2022): 1-14. Paper Link

• Zhang, Zhenrong, Jianshu Zhang, Jun Du, and Fengren Wang. "Split, embed and merge: An accurate table structure recognizer." Pattern Recognition 126 (2022): 108565. Paper Link

• Li, Xiao-Hui, Fei Yin, He-Sen Dai, and Cheng-Lin Liu. "Table Structure Recognition and Form Parsing by End-to-End Object Detection and Relation Parsing." Pattern Recognition 132 (2022): 108946. Paper Link

• Ajij, Md, Sanjoy Pratihar, Diptendu Sinha Roy, and Thomas Hanne. "Robust detection of Tables in documents using scores from Table cell cores." SN Computer Science 3, no. 2 (2022): 1-19.Paper Link

• Minouei, Mohammad, Khurram Azeem Hashmi, Mohammad Reza Soheili, Muhammad Zeshan Afzal, and Didier Stricker. "Continual Learning for Table Detection in Document Images." Applied Sciences 12, no. 18 (2022): 8969. Paper Link

• Naik, Shivam, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, and Muhammad Zeshan Afzal. "Investigating Attention Mechanism for Page Object Detection in Document Images." Applied Sciences 12, no. 15 (2022): 7486. Paper Link

Conference

• Smock, Brandon, Rohith Pesala, and Robin Abraham. "PubTables-1M: Towards comprehensive table extraction from unstructured documents." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4634-4642. 2022. Paper Link
• Namysl, Marcin, Alexander M. Esser, Sven Behnke, and Joachim Köhler. "Flexible Table Recognition and Semantic Interpretation System." In VISIGRAPP (4: VISAPP), pp. 27-37. 2022. Paper Link
• Nassar, Ahmed, Nikolaos Livathinos, Maksym Lysak, and Peter Staar. "TableFormer: Table Structure Understanding with Transformers." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4614-4623. 2022. Paper Link
• Raja, Sachin, Ajoy Mondal, and C. V. Jawahar. "Visual Understanding of Complex Table Structures from Document Images." In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2299-2308. 2022. Paper Link
• Shen, Xinyi, Lingjun Kong, Yunchao Bao, Yaowei Zhou, and Weiguang Liu. "RCANet: A Rows and Columns Aggregated Network for Table Structure Recognition." In 2022 3rd Information Communication Technologies Conference (ICTC), pp. 112-116. IEEE, 2022. Paper Link
• Li, Huichao, Lingze Zeng, Weiyu Zhang, Jianing Zhang, Ju Fan, and Meihui Zhang. "A Two-Phase Approach for Recognizing Tables with Complex Structures." In International Conference on Database Systems for Advanced Applications, pp. 587-595. Springer, Cham, 2022. Paper Link
• Kazdar, Takwa, Marwa Jmal, Wided Souidene, and Rabah Attia. "Table Recognition in Scanned Documents." In Conference on Computational Collective Intelligence Technologies and Applications, pp. 744-754. Springer, Cham, 2022. Paper Link
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Preprint

• Lin, Weihong, Zheng Sun, Chixiang Ma, Mingze Li, Jiawei Wang, Lei Sun, and Qiang Huo. "TSRFormer: Table Structure Recognition with Transformers." arXiv preprint arXiv:2208.04921 (2022). Paper Link
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2023

Journal

• Ma, Chixiang, Weihong Lin, Lei Sun, and Qiang Huo. "Robust Table Detection and Structure Recognition from Heterogeneous Document Images." Pattern Recognition 133 (2023): 109006. Paper Link

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• Kazdar, Takwa, Wided Souidene Mseddi, Moulay A. Akhloufi, Ala Agrebi, Marwa Jmal, and Rabah Attia. 2023. "DCTable: A Dilated CNN with Optimizing Anchors for Accurate Table Detection" Journal of Imaging 9, no. 3: 62. Paper Link

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• Wang, Hongyi, Yang Xue, Jiaxin Zhang, and Lianwen Jin. "Scene table structure recognition with segmentation collaboration and alignment." Pattern Recognition Letters 165 (2023): 146-153.Paper Link

Preprint

• Smock, Brandon, Rohith Pesala, and Robin Abraham. "Aligning benchmark datasets for table structure recognition." arXiv preprint arXiv:2303.00716 (2023). Paper Link

• Xing, H., Gao, F., Long, R., Bu, J., Zheng, Q., Li, L., ... & Yu, Z. (2023). LORE: Logical Location Regression Network for Table Structure Recognition. arXiv preprint arXiv:2303.03730. Paper Link

• Zhang, Z., Hu, P., Ma, J., Du, J., Zhang, J., Zhu, H., ... & Liu, C. (2023). SEMv2: Table Separation Line Detection Based on Conditional Convolution. arXiv preprint arXiv:2303.04384.Paper Link

• Ly, N. T., & Takasu, A. (2023). An End-to-End Multi-Task Learning Model for Image-based Table Recognition. arXiv preprint arXiv:2303.08648. Paper Link

• Ly, N. T., Takasu, A., Nguyen, P., & Takeda, H. (2023). Rethinking Image-based Table Recognition Using Weakly Supervised Methods. arXiv preprint arXiv:2303.07641. Paper Link

Cite as

If you find this work useful for your research, please cite our paper:

@misc{https://doi.org/10.48550/arxiv.2211.08469,
  doi = {10.48550/ARXIV.2211.08469},
  url = {https://arxiv.org/abs/2211.08469},
  author = {Kasem, Mahmoud and Abdallah, Abdelrahman and Berendeyev, Alexander and Elkady, Ebrahem and Abdalla, Mahmoud and Mahmoud, Mohamed and Hamada, Mohamed and Nurseitov, Daniyar and Taj-Eddin, Islam},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Deep learning for table detection and structure recognition: A survey},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

@article{ABDALLAH2021,
title = {TNCR: Table Net Detection and Classification Dataset},
journal = {Neurocomputing},
year = {2021},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2021.11.101},
url = {https://www.sciencedirect.com/science/article/pii/S0925231221018142},
author = {Abdelrahman Abdallah and Alexander Berendeyev and Islam Nuradin and Daniyar Nurseitov},
keywords = {Deep learning, Convolutional neural networks, Image processing, Document processing, Table detection, Page object detection},
abstract = {We present TNCR, a new table dataset with varying image quality collected from open access websites. TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes. TNCR contains 9428 labeled tables with approximately 6621 images . In this paper, we have implemented state-of-the-art deep learning-based methods for table detection to create several strong baselines. Deformable DERT with Resnet-50 Backbone Network achieves the highest performance compared to other methods with a precision of 86.7%, recall of 89.6%, and f1 score of 88.1% on the TNCR dataset. We have made TNCR open source in the hope of encouraging more deep learning approaches to table detection, classification and structure recognition. The dataset and trained model checkpoints are available at https://github.com/abdoelsayed2016/TNCR_Dataset.}
}