mlsanityData to 14 Oct 2026

Image-based table recognition: data, model, and evaluation

arXiv 1911.10683 ↗First posted 25 Nov 2019cs.CVcited by 237

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Cited by 41 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Important information that relates to a specific topic in a document is often organized in tabular format to assist readers with information retrieval and comparison, which may be difficult to provide in natural language. However, tabular data in unstructured digital documents, e.g., Portable Document Format (PDF) and images, are difficult to parse into structured machine-readable format, due to complexity and diversity in their structure and style. To facilitate image-based table recognition with deep learning, we develop the largest publicly available table recognition dataset PubTabNet (https://github.com/ibm-aur-nlp/PubTabNet), containing 568k table images with corresponding structured HTML representation. PubTabNet is automatically generated by matching the XML and PDF representations of the scientific articles in PubMed Central Open Access Subset (PMCOA). We also propose a novel attention-based encoder-dual-decoder (EDD) architecture that converts images of tables into HTML code. The model has a structure decoder which reconstructs the table structure and helps the cell decoder to recognize cell content. In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition, which more appropriately captures multi-hop cell misalignment and OCR errors than the pre-established metric. The experiments demonstrate that the EDD model can accurately recognize complex tables solely relying on the image representation, outperforming the state-of-the-art by 9.7% absolute TEDS score.

Key facts. Cited by 237 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Nov 2019.

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Citing papers per year: 2021: 21; 2022: 20; 2023: 23; 2024: 39; 2025: 52; 2026: 82.

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  1. 2021OCR-free Document Understanding Transformercited by 407
  2. 2024MinerU: An Open-Source Solution for Precise Document Content Extractioncited by 267
  3. 2024mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understandingcited by 220
  4. 2024OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoningcited by 173
  5. 2022DiT: Self-supervised Pre-training for Document Image Transformercited by 152
  6. 2024OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotationscited by 150
  7. 2023Document Understanding Dataset and Evaluation (DUDE)cited by 135
  8. 2021PubTables-1M: Towards comprehensive table extraction from unstructured documentscited by 126
  9. 2025olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Modelscited by 125
  10. 2025MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsingcited by 116
  11. 2024mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understandingcited by 114
  12. 2024LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understandingcited by 95
38 more
  1. 2022TableFormer: Table Structure Understanding with Transformerscited by 94
  2. 2025MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigmcited by 87
  3. 2022Towards Complex Document Understanding By Discrete Reasoningcited by 82
  4. 2025Dolphin: Document Image Parsing via Heterogeneous Anchor Promptingcited by 79
  5. 2024OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognitioncited by 74
  6. 2024TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergycited by 71
  7. 2024TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domainscited by 69
  8. 2021Document AI: Benchmarks, Models and Applicationscited by 62
  9. 2025SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversioncited by 53
  10. 2024Large Language Model for Table Processing: A Surveycited by 52
  11. 2021LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignmentcited by 46
  12. 2023Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluationcited by 46
  13. 2024Multimodal Table Understandingcited by 44
  14. 2024Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extractioncited by 44
  15. 2021Parsing Table Structures in the Wildcited by 43
  16. 2021Split, embed and merge: An accurate table structure recognizercited by 42
  17. 2023Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modelingcited by 41
  18. 2021PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTMLcited by 39
  19. 2024HRVDA: High-Resolution Visual Document Assistantcited by 37
  20. 2022OCR-IDL: OCR Annotations for Industry Document Library Datasetcited by 35
  21. 2024DocKylin: A Large Multimodal Model for Visual Document Understanding with Efficient Visual Slimmingcited by 35
  22. 2023Optimized Table Tokenization for Table Structure Recognitioncited by 34
  23. 2025POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversioncited by 34
  24. 2021Neural Collaborative Graph Machines for Table Structure Recognitioncited by 30
  25. 2022Robust Table Detection and Structure Recognition from Heterogeneous Document Imagescited by 30
  26. 2022GriTS: Grid table similarity metric for table structure recognitioncited by 28
  27. 2022TSRFormer: Table Structure Recognition with Transformerscited by 27
  28. 2026MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scalecited by 27
  29. 2026GLM-OCR Technical Reportcited by 26
  30. 2024SV-RAG: LoRA-Contextualizing Adaptation of MLLMs for Long Document Understandingcited by 25
  31. 2023A large-scale dataset for end-to-end table recognition in the wildcited by 24
  32. 2021Entity Relation Extraction as Dependency Parsing in Visually Rich Documentscited by 23
  33. 2024DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understandingcited by 23
  34. 2025OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Modelscited by 23
  35. 2022PP-StructureV2: A Stronger Document Analysis Systemcited by 20
  36. 2025Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understandingcited by 20
  37. 2022TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformerscited by 19
  38. 2023LORE: Logical Location Regression Network for Table Structure Recognitioncited by 19

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