mlsanityData to 7 Oct 2026

Deep learning for table detection and structure recognition: A survey

arXiv 2211.08469 ↗First posted 15 Nov 2022cs.CVcited by 14

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 63 of its 173 references that are in the map. First posted on arXiv 15 Nov 2022.

Builds on

63 of 173 references.

References · 63

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2020End-to-End Object Detection with Transformerscited by 7,384
  3. 2017Mask R-CNNcited by 7,259
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  6. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  7. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  8. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  9. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  10. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 913
  11. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  12. 2018Simple Baselines for Human Pose Estimation and Trackingcited by 502
38 more
  1. 2020ResNeSt: Split-Attention Networkscited by 445
  2. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  3. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  4. 2017Recent Trends in Deep Learning Based Natural Language Processingcited by 262
  5. 2019Image-based table recognition: data, model, and evaluationcited by 233
  6. 2015Evaluation of Deep Convolutional Nets for Document Image Classification and Retrievalcited by 212
  7. 2019High-Resolution Representations for Labeling Pixels and Regionscited by 211
  8. 2022DiT: Self-supervised Pre-training for Document Image Transformercited by 152
  9. 2020Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Contextcited by 146
  10. 2021PubTables-1M: Towards comprehensive table extraction from unstructured documentscited by 122
  11. 2020Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Trainingcited by 103
  12. 2019TableBank: A Benchmark Dataset for Table Detection and Recognitioncited by 102
  13. 2020CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentscited by 98
  14. 2016DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Modelcited by 96
  15. 2022TableFormer: Table Structure Understanding with Transformerscited by 93
  16. 2019Complicated Table Structure Recognitioncited by 91
  17. 2020TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Imagescited by 69
  18. 2017Learning Feature Pyramids for Human Pose Estimationcited by 57
  19. 2020Table Structure Recognition using Top-Down and Bottom-Up Cuescited by 53
  20. 2018MaskRNN: Instance Level Video Object Segmentationcited by 48
  21. 2021LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignmentcited by 46
  22. 2021Parsing Table Structures in the Wildcited by 43
  23. 2021Split, embed and merge: An accurate table structure recognizercited by 42
  24. 2018Software Engineering Challenges of Deep Learningcited by 41
  25. 2018Extracting Scientific Figures with Distantly Supervised Neural Networkscited by 36
  26. 2017Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developmentscited by 30
  27. 2022Robust Table Detection and Structure Recognition from Heterogeneous Document Imagescited by 30
  28. 2022GriTS: Grid table similarity metric for table structure recognitioncited by 27
  29. 2019IoU-balanced Loss Functions for Single-stage Object Detectioncited by 22
  30. 2020CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Imagescited by 22
  31. 2020Table Structure Extraction with Bi-directional Gated Recurrent Unit Networkscited by 20
  32. 2021Visual Understanding of Complex Table Structures from Document Imagescited by 17
  33. 2020IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documentscited by 16
  34. 2018A Saliency-based Convolutional Neural Network for Table and Chart Detection in Digitized Documentscited by 15
  35. 2021Guided Table Structure Recognition through Anchor Optimizationcited by 15
  36. 2021TNCR: Table Net Detection and Classification Datasetcited by 14
  37. 2020HKR For Handwritten Kazakh & Russian Databasecited by 12
  38. 2021TableSense: Spreadsheet Table Detection with Convolutional Neural Networkscited by 12

50 most cited of 63 All 63 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 4; 2026: 4.

14 papers.

Sort:

By area:

  1. 2024Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extractioncited by 44
  2. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  3. 2025Multimodal Tabular Reasoning with Privileged Structured Informationcited by 14
  4. 2024Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasonercited by 9
  5. 2023Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Surveycited by 8
  6. 2024Transformers and Language Models in Form Understanding: A Comprehensive Review of Scanned Document Analysiscited by 6
  7. 2023Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketingcited by 3
  8. 2024A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmaskingcited by 1
  9. 2025OG-HFYOLO :Orientation gradient guidance and heterogeneous feature fusion for deformation table cell instance segmentationcited by 1
  10. 2026Beyond String Matching: Semantic Evaluation of PDF Table Extractioncited by 1
  11. 2025Edge-Native Digitization of Handwritten Marksheets: A Hybrid Heuristic-Deep Learning Frameworkcited by 0
  12. 2026When Good OCR Is Not Enough: Benchmarking OCR Robustness for Retrieval-Augmented Generationcited by 0
2 more
  1. 2026ConRTF: Edge-Constrained Boundary Distribution Refinement for Realtime TransFormer Table Structure Recognitioncited by 0
  2. 2026Active Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelinescited by 0