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We present TNCR, a new table dataset with varying image quality collected from free websites.
M. Li, L. Cui, S. Huang, F. Wei, M. Zhou, Z. Li, Tablebank: Table benchmark for image-based table detection and recognition, in: Proceedings of the 12th Language Resources and Evaluation Conference, 2020, pp. 1918–1925
1925
Earlier work this paper cites.
K. Itonori, 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, 1993, pp. 765–768
1993
Earlier work this paper cites.
S. Chandran, R. Kasturi, Structural recognition of tabulated data, in: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), IEEE, 1993, pp. 516–519
1993
Earlier work this paper cites.
Y. LeCun, Y. Bengio, et al., Convolutional networks for images, speech, and time series, The handbook of brain theory and neural networks 3361 (10) (1995) 1995
1995
Earlier work this paper cites.
S. Lawrence, C. L. Giles, A. C. Tsoi, A. D. Back, Face recognition: A convolutional neural-network approach, IEEE transactions on neural networks 8 (1) (1997) 98–113
1997
Earlier work this paper cites.
T. Kieninger, A. Dengel, The t-recs table recognition and analysis system, Vol. 1655, 1998, pp. 255–269
1998
Earlier work this paper cites.
F. Cesarini, S. Marinai, L. Sarti, G. Soda, Trainable table location in document images, in: Object recognition supported by user interaction for service robots, Vol. 3, IEEE, 2002, pp. 236–240
2002
Earlier work this paper cites.
S. Mao, A. Rosenfeld, T. Kanungo, Document structure analysis algorithms: a literature survey, in: Document Recognition and Retrieval X, Vol. 5010, International Society for Optics and Photonics, 2003, pp. 197–207
2003
Earlier work this paper cites.
A. C. e Silva, Learning rich hidden markov models in document analysis: Table location, in: 2009 10th International Conference on Document Analysis and Recognition, IEEE, 2009, pp. 843–847
2009
Earlier work this paper cites.
A. Shahab, F. Shafait, T. Kieninger, A. Dengel, An open approach towards the benchmarking of table structure recognition systems, in: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, 2010, pp. 113–120
2010
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, A. Zisserman, The pascal visual object classes (voc) challenge, International journal of computer vision 88 (2) (2010) 303–338
2010
Earlier work this paper cites.
J. Fang, X. Tao, Z. Tang, R. Qiu, Y. Liu, Dataset, ground-truth and performance metrics for table detection evaluation, in: 2012 10th IAPR International Workshop on Document Analysis Systems, IEEE, 2012, pp. 445–449
2012
Earlier work this paper cites.
M. Göbel, T. Hassan, E. Oro, G. Orsi, Icdar 2013 table competition, in: 2013 12th International Conference on Document Analysis and Recognition, IEEE, 2013, pp. 1449–1453
2013
Earlier work this paper cites.
doi:10.1109/ICDAR.2013.240
T. Kasar, P. Barlas, S. Adam, C. Chatelain, T. Paquet, Learning to detect tables in scanned document images using line information, in: 2013 12th International Conference on Document Analysis and Recognition, 2013, pp. 1185–1189 · 2013
Earlier work this paper cites.
O. Abdel-Hamid, A.-r. Mohamed, H. Jiang, L. Deng, G. Penn, D. Yu, Convolutional neural networks for speech recognition, IEEE/ACM Transactions on audio, speech, and language processing 22 (10) (2014) 1533–1545
2014
Earlier work this paper cites.
Q. Li, W. Cai, X. Wang, Y. Zhou, D. D. Feng, M. Chen, Medical image classification with convolutional neural network, in: 2014 13th international conference on control automation robotics & vision (ICARCV), IEEE, 2014, pp. 844–848
2014
Earlier work this paper cites.
D. N. Tran, T. A. Tran, A. Oh, S. H. Kim, I. S. Na, Table detection from document image using vertical arrangement of text blocks, International Journal of Contents 11 (4) (2015) 77–85
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
W. Seo, H. I. Koo, N. I. Cho, Junction-based table detection in camera-captured document images, International Journal on Document Analysis and Recognition (IJDAR) 18 (1) (2015) 47–57
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431–3440
2015
Cited alongside, same era.
doi:10.1109/DAS.2016.23
L. Hao, L. Gao, X. Yi, Z. Tang, A table detection method for pdf documents based on convolutional neural networks, in: 2016 12th IAPR Workshop on Document Analysis Systems (DAS), 2016, pp. 287–292 · 2016
Cited alongside, same era.
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778
2016
Cited alongside, same era.
doi:10.1109/ICDAR.2017.192
S. Schreiber, S. Agne, I. Wolf, A. Dengel, S. 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. 01, 2017, pp. 1162–1167 · 2017
K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Shi, W. Ouyang, C. C. Loy, D. Lin, Hybrid task cascade for instance segmentation, in: IEEE Conference on Computer Vision and Pattern Recognition, 2019
2019
Later among the works it cites.
K. Sun, B. Xiao, D. Liu, J. Wang, Deep high-resolution representation learning for human pose estimation, in: CVPR, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
doi:10.5281/zenodo.2649217
H. Déjean, J.-L. Meunier, L. Gao, Y. Huang, Y. Fang, F. Kleber, E.-M. Lang, ICDAR 2019 Competition on Table Detection and Recognition (cTDaR) , http://sac.founderit.com/ (Apr. 2019) · 2019
Later among the works it cites.
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Cited alongside, same era.
A. Gilani, S. R. Qasim, I. Malik, F. Shafait, Table detection using deep learning, in: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), Vol. 1, IEEE, 2017, pp. 771–776
2017
Cited alongside, same era.
J. Gehring, M. Auli, D. Grangier, D. Yarats, Y. N. Dauphin, Convolutional sequence to sequence learning, in: International Conference on Machine Learning, PMLR, 2017, pp. 1243–1252
2017
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollar, R. Girshick, Mask r-cnn, 2017 IEEE International Conference on Computer Vision (ICCV) (Oct 2017)
2017
Cited alongside, same era.
S. Xie, R. Girshick, P. Dollár, Z. Tu, K. He, Aggregated residual transformations for deep neural networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1492–1500
2017
Cited alongside, same era.
J. Redmon, A. Farhadi, Yolov3: An incremental improvement (2018) · 2018
Cited alongside, same era.
N. Siegel, N. Lourie, R. Power, W. Ammar, Extracting scientific figures with distantly supervised neural networks, in: Proceedings of the 18th ACM/IEEE on joint conference on digital libraries, 2018, pp. 223–232
2018
Cited alongside, same era.
S. Arif, F. Shafait, Table detection in document images using foreground and background features, in: 2018 Digital Image Computing: Techniques and Applications (DICTA), IEEE, 2018, pp. 1–8
2018
Cited alongside, same era.
2019
Later among the works it cites.
S. S. Paliwal, D. Vishwanath, R. Rahul, M. Sharma, L. 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), IEEE, 2019, pp. 128–133
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
E. Kara, M. Traquair, M. Simsek, B. Kantarci, S. Khan, Holistic design for deep learning-based discovery of tabular structures in datasheet images, Engineering Applications of Artificial Intelligence 90 (2020) 103551
2020
Later among the works it cites.
doi:10.1145/3410352.3410744
A. Abdallah, M. Kasem, M. A. Hamada, S. Sdeek, Automated question-answer medical model based on deep learning technology , in: Proceedings of the 6th International Conference on Engineering & MIS 2020, ICEMIS’20, Association for Computing Machinery, New York, NY, USA, 2020 · 2020
Later among the works it cites.
doi:10.3390/jimaging6120141
A. Abdallah, M. Hamada, D. Nurseitov, Attention-based fully gated cnn-bgru for russian handwritten text , Journal of Imaging 6 (12) (2020) 141 · 2020
Later among the works it cites.
2020
Later among the works it cites.
doi:10.25046/aj0505114
G. A. Daniyar Nurseitov, Kairat Bostanbekov, Maksat Kanatov, Anel Alimova, Abdelrahman Abdallah, Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models, Advances in Science, Technology and Engineering Systems Journal 5 (5) (2020) 934–943 · 2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
D. Prasad, A. Gadpal, K. Kapadni, M. Visave, K. 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, 2020, pp. 572–573
2020
Later among the works it cites.
J. Jiang, M. Simsek, B. Kantarci, S. Khan, Tabcellnet: Deep learning-based tabular cell structure detection, Neurocomputing 440 (2021) 12–23
2021
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