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PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML

arXiv 2105.01848 ↗First posted 5 May 2021cs.CV cs.AIcited by 39

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Abstract (arXiv)

This paper presents our solution for ICDAR 2021 competition on scientific literature parsing taskB: table recognition to HTML. In our method, we divide the table content recognition task into foursub-tasks: table structure recognition, text line detection, text line recognition, and box assignment.Our table structure recognition algorithm is customized based on MASTER [1], a robust image textrecognition algorithm. PSENet [2] is used to detect each text line in the table image. For text linerecognition, our model is also built on MASTER. Finally, in the box assignment phase, we associatedthe text boxes detected by PSENet with the structure item reconstructed by table structure prediction,and fill the recognized content of the text line into the corresponding item. Our proposed methodachieves a 96.84% TEDS score on 9,115 validation samples in the development phase, and a 96.32%TEDS score on 9,064 samples in the final evaluation phase.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 5 May 2021.

Builds on

9 of 13 references.

References · 9

  1. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 763
  2. 2019Lookahead Optimizer: k steps forward, 1 step backcited by 333
  3. 2018Context Encoding for Semantic Segmentationcited by 289
  4. 2019Image-based table recognition: data, model, and evaluationcited by 233
  5. 2019Shape Robust Text Detection with Progressive Scale Expansion Networkcited by 147
  6. 2020Gradient Centralization: A New Optimization Technique for Deep Neural Networkscited by 82
  7. 2019MASTER: Multi-Aspect Non-local Network for Scene Text Recognitioncited by 65
  8. 2018Exploiting Deep Representations for Neural Machine Translationcited by 23
  9. 2020Learning Graph Normalization for Graph Neural Networkscited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 11; 2024: 10; 2025: 9; 2026: 7.

39 papers.

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  1. 2024MinerU: An Open-Source Solution for Precise Document Content Extractioncited by 255
  2. 2025MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigmcited by 81
  3. 2024OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognitioncited by 74
  4. 2023Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluationcited by 46
  5. 2024Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extractioncited by 44
  6. 2023Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modelingcited by 41
  7. 2023Optimized Table Tokenization for Table Structure Recognitioncited by 34
  8. 2023A large-scale dataset for end-to-end table recognition in the wildcited by 24
  9. 2025OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Modelscited by 22
  10. 2023LORE: Logical Location Regression Network for Table Structure Recognitioncited by 19
  11. 2022PP-StructureV2: A Stronger Document Analysis Systemcited by 18
  12. 2023An End-to-End Multi-Task Learning Model for Image-based Table Recognitioncited by 14
27 more
  1. 2023GridFormer: Towards Accurate Table Structure Recognition via Grid Predictioncited by 13
  2. 2024UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretrainingcited by 12
  3. 2023Rethinking Image-based Table Recognition Using Weakly Supervised Methodscited by 10
  4. 2025TFLOP: Table Structure Recognition Framework with Layout Pointer Mechanismcited by 9
  5. 2024LORE++: Logical Location Regression Network for Table Structure Recognition with Pre-trainingcited by 8
  6. 2025Table2LaTeX-RL: High-Fidelity LaTeX Code Generation from Table Images via Reinforced Multimodal Language Modelscited by 7
  7. 2025SPRINT: Script-agnostic Structure Recognition in Tablescited by 6
  8. 2024TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Contentcited by 5
  9. 2024Multi-Cell Decoder and Mutual Learning for Table Structure and Character Recognitioncited by 5
  10. 2025TABLET: Table Structure Recognition using Encoder-only Transformerscited by 5
  11. 2023High-Performance Transformers for Table Structure Recognition Need Early Convolutionscited by 4
  12. 2024Synthesizing Realistic Data for Table Recognitioncited by 2
  13. 20211st Place Solution for ICDAR 2021 Competition on Mathematical Formula Detectioncited by 1
  14. 2023Rethinking Detection Based Table Structure Recognition for Visually Rich Document Imagescited by 1
  15. 2024PdfTable: A Unified Toolkit for Deep Learning-Based Table Extractioncited by 1
  16. 2025TableCenterNet: A one-stage network for table structure recognitioncited by 1
  17. 2026TDATR: Improving End-to-End Table Recognition via Table Detail-Aware Learning and Cell-Level Visual Alignmentcited by 1
  18. 2026InstructTable: Improving Table Structure Recognition Through Instructionscited by 1
  19. 2023TabIQA: Table Questions Answering on Business Document Imagescited by 0
  20. 2024Self-Supervised Pre-Training for Table Structure Recognition Transformercited by 0
  21. 2025zERExtractor:An Automated Platform for Enzyme-Catalyzed Reaction Data Extraction from Scientific Literaturecited by 0
  22. 2025Hierarchical Modeling Approach to Fast and Accurate Table Recognitioncited by 0
  23. 2026TableSeq: Unified Generation of Structure, Content, and Layoutcited by 0
  24. 2026FastTab: A Fast Table Recognizer with a Tiny Recursive Module and 1D Transformerscited by 0
  25. 2026Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representationscited by 0
  26. 2026Rethinking the Pointer Loss in Table Structure Recognition: Geometry-Aware Pointer Loss for Spatial Localitycited by 0
  27. 2026Enhancing Table Structure Recognition via Bounding Box Guidancecited by 0