Table Recognition

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

5 leaderboard tables shown for this task, 5 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

7 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

28 shown of 28 papers with code (50 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Image-based table recognition: data, model, and evaluation
  25 Nov 2019 6 repositories listed Syntology ran 2 of 6 samples · 4 unverified
  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.
• Rethinking Table Recognition using Graph Neural Networks
  31 May 2019 5 repositories listed
  In this paper, we propose an architecture based on graph networks as a better alternative to standard neural networks for table recognition.
• Deep learning for table detection and structure recognition: A survey
  15 Nov 2022 3 repositories listed
  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…
• ICDAR 2021 Competition on Scientific Literature Parsing
  8 Jun 2021 3 repositories listed Syntology ran 3 of 8 samples · 5 unverified · 1 pointer-only (licence)
  Scientific literature contain important information related to cutting-edge innovations in diverse domains.
• PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML
  5 May 2021 3 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  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.
• CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents
  27 Apr 2020 3 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model.
• High-Performance Transformers for Table Structure Recognition Need Early Convolutions
  9 Nov 2023 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  This allows it to "see" an appropriate portion of the table and "store" the complex table structure within sufficient context length for the subsequent transformer.
• An End-to-End Multi-Task Learning Model for Image-based Table Recognition
  15 Mar 2023 2 repositories listed
  Most of the previous methods focus on a non-end-to-end approach which divides the problem into two separate sub-problems: table structure recognition; and cell-content recognition and then attempts to solve each…
• LORE: Logical Location Regression Network for Table Structure Recognition
  7 Mar 2023 2 repositories listed
  Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats.
• PubTables-1M: Towards comprehensive table extraction from unstructured documents
  30 Sep 2021 2 repositories listed Syntology ran 5 of 13 samples · 8 unverified · 10 pointer-only (licence)
  We demonstrate that these improvements lead to a significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition.
• LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
  13 May 2021 2 repositories listed
  In this paper, we aim to obtain more reliable aligned bounding boxes by fully utilizing the visual information from both text regions in proposed local features and cell relations in global features.
• OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models
  22 Feb 2025 1 repository listed
  In this paper, we introduce OmniParser V2, a universal model that unifies VsTP typical tasks, including text spotting, key information extraction, table recognition, and layout analysis, into a unified framework.
• Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
  30 Dec 2024 1 repository listed
  Pre-trained foundation models have recently significantly progressed in structured table understanding and reasoning.
• PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction
  8 Sep 2024 1 repository listed
  To address these issues, we have introduced the PDF table extraction (PdfTable) toolkit.
• Multi-Cell Decoder and Mutual Learning for Table Structure and Character Recognition
  20 Apr 2024 1 repository listed
  Extracting table contents from documents such as scientific papers and financial reports and converting them into a format that can be processed by large language models is an important task in knowledge information…
• Synthesizing Realistic Data for Table Recognition
  17 Apr 2024 1 repository listed
  By leveraging the actual structure and content of tables from Chinese financial announcements, we have developed the first extensive table annotation dataset in this domain.
• OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition
  28 Mar 2024 1 repository listed Syntology ran 3 of 4 samples · 1 unverified
  Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs)…
• UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining
  7 Mar 2024 1 repository listed Syntology ran 4 of 4 samples · 0 unverified
  We present UniTable, a training framework that unifies both the training paradigm and training objective of TR.
• OmniParser: A Unified Framework for Text Spotting Key Information Extraction and Table Recognition
  1 Jan 2024 1 repository listed
  Recently visually-situated text parsing (VsTP) has experienced notable advancements driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs)…
• A large-scale dataset for end-to-end table recognition in the wild
  27 Mar 2023 1 repository listed
  To this end, we propose a new large-scale dataset named Table Recognition Set (TabRecSet) with diverse table forms sourcing from multiple scenarios in the wild, providing complete annotation dedicated to end-to-end TR…
• Rethinking Image-based Table Recognition Using Weakly Supervised Methods
  14 Mar 2023 1 repository listed
  In this paper, we propose a weakly supervised model named WSTabNet for table recognition that relies only on HTML (or LaTeX) code-level annotations of table images.
• Aligning benchmark datasets for table structure recognition
  1 Mar 2023 1 repository listed
  However, even if a dataset's annotations are self-consistent, there may be significant inconsistency across datasets, which can harm the performance of models trained and evaluated on them.
• PP-StructureV2: A Stronger Document Analysis System
  11 Oct 2022 1 repository listed
  For Table Recognition model, we utilize PP-LCNet, CSP-PAN and SLAHead to optimize the backbone module, feature fusion module and decoding module, respectively, which improved the table structure accuracy by 6\% with…
• Detecting Layout Templates in Complex Multiregion Files
  14 Sep 2021 1 repository listed
  We present the Mondrian approach to automatically identify layout templates across multiple files and systematically extract the corresponding regions.
• TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition
  20 Jun 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  A table arranging data in rows and columns is a very effective data structure, which has been widely used in business and scientific research.
• Flexible Table Recognition and Semantic Interpretation System
  25 May 2021 1 repository listed
  Moreover, to incorporate the extraction of semantic information, we develop a graph-based table interpretation method.
• Multi-Type-TD-TSR -- Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table Representations
  23 May 2021 1 repository listed
  It utilizes state-of-the-art deep learning models for table detection and differentiates between 3 different types of tables based on the tables' borders.
• Table Structure Recognition using Top-Down and Bottom-Up Cues
  9 Oct 2020 1 repository listed
  We present an approach for table structure recognition that combines cell detection and interaction modules to localize the cells and predict their row and column associations with other detected cells.

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.