mlsanityData to 7 Oct 2026

Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context

arXiv 2005.00589 ↗First posted 1 May 2020cs.CVcited by 146

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Abstract (arXiv)

Documents are often used for knowledge sharing and preservation in business and science, within which are tables that capture most of the critical data. Unfortunately, most documents are stored and distributed as PDF or scanned images, which fail to preserve logical table structure. Recent vision-based deep learning approaches have been proposed to address this gap, but most still cannot achieve state-of-the-art results. We present Global Table Extractor (GTE), a vision-guided systematic framework for joint table detection and cell structured recognition, which could be built on top of any object detection model. With GTE-Table, we invent a new penalty based on the natural cell containment constraint of tables to train our table network aided by cell location predictions. GTE-Cell is a new hierarchical cell detection network that leverages table styles. Further, we design a method to automatically label table and cell structure in existing documents to cheaply create a large corpus of training and test data. We use this to enhance PubTabNet with cell labels and create FinTabNet, real-world and complex scientific and financial datasets with detailed table structure annotations to help train and test structure recognition. Our framework surpasses previous state-of-the-art results on the ICDAR 2013 and ICDAR 2019 table competition in both table detection and cell structure recognition with a significant 5.8% improvement in the full table extraction system. Further experiments demonstrate a greater than 45% improvement in cell structure recognition when compared to a vanilla RetinaNet object detection model in our new out-of-domain FinTabNet.

Key facts. Cited by 146 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 May 2020.

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Citing papers per year: 2021: 13; 2022: 14; 2023: 28; 2024: 28; 2025: 31; 2026: 32.

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  1. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 552
  2. 2024OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoningcited by 165
  3. 2023Document Understanding Dataset and Evaluation (DUDE)cited by 132
  4. 2021PubTables-1M: Towards comprehensive table extraction from unstructured documentscited by 122
  5. 2022MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Datacited by 120
  6. 2021TABBIE: Pretrained Representations of Tabular Datacited by 111
  7. 2025MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsingcited by 108
  8. 2022TableFormer: Table Structure Understanding with Transformerscited by 93
  9. 2025MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigmcited by 81
  10. 2024A Survey of Large Language Models in Finance (FinLLMs)cited by 77
  11. 2024OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognitioncited by 74
  12. 2024TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergycited by 70
38 more
  1. 2024TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domainscited by 68
  2. 2023Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Taskscited by 59
  3. 2023DocILE Benchmark for Document Information Localization and Extractioncited by 58
  4. 2025SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversioncited by 51
  5. 2021LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignmentcited by 46
  6. 2024Financial Report Chunking for Effective Retrieval Augmented Generationcited by 46
  7. 2024Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extractioncited by 44
  8. 2021Parsing Table Structures in the Wildcited by 43
  9. 2022Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Taskscited by 42
  10. 2023Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modelingcited by 41
  11. 2023Optimized Table Tokenization for Table Structure Recognitioncited by 34
  12. 2025REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmarkcited by 34
  13. 2021Neural Collaborative Graph Machines for Table Structure Recognitioncited by 30
  14. 2022Robust Table Detection and Structure Recognition from Heterogeneous Document Imagescited by 30
  15. 2022TSRFormer: Table Structure Recognition with Transformerscited by 27
  16. 2024PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Modelscited by 25
  17. 2021Retrieving Complex Tables with Multi-Granular Graph Representation Learningcited by 24
  18. 2025OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Modelscited by 22
  19. 2021Automated scholarly paper review: Concepts, technologies, and challengescited by 21
  20. 2025Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligencecited by 20
  21. 2022TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformerscited by 19
  22. 2023LORE: Logical Location Regression Network for Table Structure Recognitioncited by 19
  23. 2021Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networkscited by 18
  24. 2022PP-StructureV2: A Stronger Document Analysis Systemcited by 18
  25. 2023DocTr: Document Transformer for Structured Information Extraction in Documentscited by 18
  26. 2024MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understandingcited by 18
  27. 2021Visual Understanding of Complex Table Structures from Document Imagescited by 17
  28. 2024TQA-Bench: Evaluating LLMs for Multi-Table Question Answeringcited by 17
  29. 2025Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsingcited by 17
  30. 2025Logics-Parsing Technical Reportcited by 16
  31. 2023Aligning benchmark datasets for table structure recognitioncited by 15
  32. 2025NVIDIA Nemotron Nano V2 VLcited by 15
  33. 2022Deep learning for table detection and structure recognition: A surveycited by 14
  34. 2023An End-to-End Multi-Task Learning Model for Image-based Table Recognitioncited by 14
  35. 2023GridFormer: Towards Accurate Table Structure Recognition via Grid Predictioncited by 13
  36. 2023SEMv2: Table Separation Line Detection Based on Instance Segmentationcited by 12
  37. 2023Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformercited by 12
  38. 2024UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretrainingcited by 12

50 most cited of 146 All 146 (JSON)