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Recognizing the layout of unstructured digital documents is an important step when parsing the documents into structured machine-readable format for downstream applications.
V. I. Levenshtein, “Binary codes capable of correcting deletions, insertions, and reversals,” in Soviet physics doklady , vol. 10, no. 8, 1966, pp. 707–710
1966
Earlier work this paper cites.
R. Cattoni, T. Coianiz, S. Messelodi, and C. M. Modena, “Geometric layout analysis techniques for document image understanding: a review,” ITC-irst Technical Report , vol. 9703, no. 09, 1998
1998
Earlier work this paper cites.
T. M. Breuel, “Two geometric algorithms for layout analysis,” in International workshop on document analysis systems . Springer, 2002, pp. 188–199
2002
Earlier work this paper cites.
——, “High performance document layout analysis,” in Proceedings of the Symposium on Document Image Understanding Technology , 2003, pp. 209–218
2003
Earlier work this paper cites.
B. Yildiz, K. Kaiser, and S. Miksch, “pdf2table: A method to extract table information from pdf files,” in IICAI , 2005, pp. 1773–1785
2005
Earlier work this paper cites.
A. Antonacopoulos, D. Bridson, C. Papadopoulos, and S. Pletschacher, “A realistic dataset for performance evaluation of document layout analysis,” in Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference on . IEEE, 2009, pp. 296–300
2009
Earlier work this paper cites.
A. Shahab, F. Shafait, T. Kieninger, and 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 . ACM, 2010, pp. 113–120
2010
Earlier work this paper cites.
A. Silva, “Parts that add up to a whole: a framework for the analysis of tables,” Edinburgh University, UK , 2010
2010
Earlier work this paper cites.
J. Fang, X. Tao, Z. Tang, R. Qiu, and Y. Liu, “Dataset, ground-truth and performance metrics for table detection evaluation,” in Document Analysis Systems (DAS), 2012 10th IAPR International Workshop on . IEEE, 2012, pp. 445–449
2012
Cited alongside, same era.
M. Göbel, T. Hassan, E. Oro, and G. Orsi, “Icdar 2013 table competition,” in Document Analysis and Recognition (ICDAR), 2013 12th International Conference on . IEEE, 2013, pp. 1449–1453
2013
Cited alongside, same era.
C. Clausner, C. Papadopoulos, S. Pletschacher, and A. Antonacopoulos, “The enp image and ground truth dataset of historical newspapers,” in Document Analysis and Recognition (ICDAR), 2015 13th International Conference on . IEEE, 2015, pp. 931–935
2015
Cited alongside, same era.
D. N. Tran, T. A. Tran, A. Oh, S. H. Kim, and I. S. Na, “Table detection from document image using vertical arrangement of text blocks,” International Journal of Contents , vol. 11, no. 4, pp. 77–85, 2015
2015
Cited alongside, same era.
S. Schreiber, S. Agne, I. Wolf, A. Dengel, and S. Ahmed, “Deepdesrt: Deep learning for detection and structure recognition of tables in document images,” in Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on , vol. 1. IEEE, 2017, pp. 1162–1167
2017
Later among the works it cites.
C. Clausner, A. Antonacopoulos, and S. Pletschacher, “Icdar2017 competition on recognition of documents with complex layouts-rdcl2017,” in Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on , vol. 1. IEEE, 2017, pp. 1404–1410
2017
Later among the works it cites.
D. He, S. Cohen, B. Price, D. Kifer, and C. L. Giles, “Multi-scale multi-task fcn for semantic page segmentation and table detection,” in Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on , vol. 1. IEEE, 2017, pp. 254–261
2017
Later among the works it cites.
A. Gilani, S. R. Qasim, I. Malik, and F. Shafait, “Table detection using deep learning,” in 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , vol. 01, Nov 2017, pp. 771–776
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S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
Cited alongside, same era.
L. Hao, L. Gao, X. Yi, and Z. Tang, “A table detection method for pdf documents based on convolutional neural networks,” in Document Analysis Systems (DAS), 2016 12th IAPR Workshop on . IEEE, 2016, pp. 287–292
2016
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Computer Vision (ICCV), 2017 IEEE International Conference on . IEEE, 2017, pp. 2980–2988
2017
Cited alongside, same era.
2017
Later among the works it cites.
P. W. J. Staar, M. Dolfi, C. Auer, and C. Bekas, “Corpus conversion service: A machine learning platform to ingest documents at scale,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , ser. KDD ’18. New York, NY, USA: ACM, 2018, pp. 774–782. [Online]. Available: http://doi.acm.org/10.1145/3219819.3219834
2018
Later among the works it cites.
2018
Later among the works it cites.
R. Girshick, I. Radosavovic, G. Gkioxari, P. Dollár, and K. He, “Detectron,” https://github.com/facebookresearch/detectron
2018
Later among the works it cites.