# ICDAR 2021 SLP Task B: Table Recognition on PubTabNet > Competition overview for Task B of the ICDAR 2021 Scientific Literature Parsing competition: table structure recognition and cell content extraction on PubTabNet, evaluated with TEDS. - Authors: Hunter Heidenreich - Published: 2026-04-01 - Paper: Preprint (arXiv) - Paper: Published (ICDAR 2021) - Code: ICDAR2021-SLP (GitHub) - Source: Bevaya Labs, --- ## TL;DR ICDAR 2021 SLP Task B is the competition track for full table recognition (structure plus cell content) on PubTabNet. With 30 teams and 30 final-phase submissions, the top systems reached 96.36 TEDS overall (97.88 on simple tables, 94.78 on complex), well above the 91 TEDS baseline reported with PubTabNet's original EDD model. Two-stage pipelines (cell detection then structure inference) dominated over end-to-end sequence methods. ## What kind of paper is this? **Dominant:** $\Psi\_{\text{Resource}}$: The primary contribution is a competition benchmark event. The paper documents the evaluation protocol, participation statistics, leaderboard results, and system descriptions. No new model or dataset is introduced; PubTabNet (v2.0.0) provides the underlying data. **Secondary:** none. ## What is the motivation? Prior table recognition competitions (ICDAR 2013, ICDAR 2019) were small (156 and 3,600 samples respectively) and did not require systems to recover cell content, only cell bounding boxes or adjacency relations. Task B raised the bar: participants must produce a complete HTML representation of each table image, including both structure (row/column spans) and the text content of every cell. No intermediate cell-position annotations are provided during evaluation, pushing systems toward truly end-to-end solutions. The competition is part of a two-task event (ICDAR2021-SLP). Task A, which addresses document layout recognition on PubLayNet, is covered in the [layout notes](../layout/). ## What is the novelty? The competition establishes a documented, large-scale snapshot of the state of full table recognition in 2021, with TEDS as the community-standard metric. The system descriptions from the top teams reveal the architectural patterns that worked best at scale, serving as a useful reference for understanding the field's trajectory toward later models like TableFormer, TRUST, and UniTable. ## What experiments were performed? ### Data Task B used PubTabNet v2.0.0. The final evaluation set (9,064 images) was released three days before the competition deadline. Participants had access to training (500,777 samples) and development (9,115 samples) sets throughout. | Split | Size | Phase | | :--- | ---: | :--- | | Training | 500,777 | N/A | | Development | 9,115 | N/A | | Mini development | 20 | Format verification | | Test | 9,138 | Development phase | | Final evaluation | 9,064 | Final evaluation phase | ### Metric TEDS (Tree-Edit-Distance-based Similarity), the metric introduced with PubTabNet. It computes normalized tree edit distance between the predicted and ground-truth HTML table trees, including cell content: $$\text{TEDS}(T\_a, T\_b) = 1 - \frac{\text{EditDist}(T\_a, T\_b)}{\max(|T\_a|, |T\_b|)}$$ Results are decomposed into Simple and Complex subsets following PubTabNet's original split. **Known evaluation bug:** Bold tags (``) were inadvertently excluded from scoring in the final evaluation phase due to a data preparation issue. All reported numbers are therefore slightly inflated for tables containing bold text. This affects all teams equally, so relative rankings hold, but absolute TEDS numbers are not directly comparable to evaluations that include bold tags. ### Results Top 9 teams on the Final Evaluation Phase leaderboard: | Team | TEDS Simple | TEDS Complex | TEDS All | | :--- | ---: | ---: | ---: | | Davar-Lab-OCR (Hikvision) | 97.88 | 94.78 | **96.36** | | VCGroup | **97.90** | 94.68 | 96.32 | | XM | 97.60 | **94.89** | 96.27 | | YG | 97.38 | 94.79 | 96.11 | | DBJ | 97.39 | 93.87 | 95.66 | | TAL | 97.30 | 93.93 | 95.65 | | PaodingAI | 97.35 | 93.79 | 95.61 | | anyone | 96.95 | 93.43 | 95.23 | | LTIAYN | 97.18 | 92.40 | 94.84 | Prior state of the art (EDD model, reported with PubTabNet): 91 TEDS. ## What are the outcomes/conclusions? **Key findings:** - Complex tables are consistently 3-4 points harder than simple ones across all teams. The gap is stable, suggesting it reflects genuine structural difficulty rather than a calibration artifact. - Two-stage pipelines dominated: detect cells (Mask R-CNN variants, often HRNet backbone with pyramid mask supervision), then infer structure by horizontally/vertically aligning bounding boxes via maximum clique search or similar combinatorial post-processing. This approach cleanly separates cell localization from topology recovery. - One team (VCGroup) decomposed the task into four sub-tasks: table structure recognition, text-line detection, text-line recognition, and box assignment. Their structure recognizer was based on MASTER (a scene text recognition model), demonstrating that sequence models from adjacent domains transferred well. - A pure sequence approach (Kaen Context, Kakao Enterprise) using a 12-layer linear-attention transformer operating on flattened image patches also competed, anticipating the later EDD/TableFormer direction. - Performance improvement over EDD (91 TEDS) is substantial (~5 points), driven by larger backbones, ensemble strategies, and better OCR modules. **Limitations:** - The `` bold tag bug means all reported TEDS numbers are slightly inflated. The magnitude of the effect depends on how bold-heavy the evaluation tables are; the authors acknowledge it but do not quantify the impact. - Only 30 teams participated in the final phase, far fewer than Task A (78 teams). Table recognition attracted less participation, likely due to higher implementation complexity. - The competition uses PubTabNet, which is scientific literature only. Transferability to financial tables (FinTabNet) or wild tables (WTW) is not evaluated. - No source code was released by competition organizers; most system descriptions are high-level. ## Reproducibility ### Models No trained weights were released by the competition organizers or most participating teams. Davar-Lab-OCR referenced their lab website and later released code via DAVAR-Lab-OCR GitHub; VCGroup released MASTER-pytorch (the backbone of their structure recognizer). ### Algorithms Top two-stage systems: Cascade Mask R-CNN with HRNet-W48 backbone for cell detection (with pyramid mask supervision for row/column-aligned bounding boxes), followed by structure inference via alignment overlap analysis and maximum clique search for row/column index assignment. OCR was handled by a separate single-line text detection and recognition model. Optimizer, learning rate schedule, batch size, and other training hyperparameters are not reported in the competition overview paper; individual teams did not provide full training details in their system descriptions. ### Data PubTabNet v2.0.0 is publicly available. Annotations are under CDLA-Permissive-1.0; underlying PMCOA images have mixed per-article licenses. See [PubTabNet notes](2019-11-pubtabnet.md) for full dataset details. ### Evaluation TEDS computation code is available in the [PubTabNet repository](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src). Competition evaluation harness at [ICDAR2021-SLP GitHub](https://github.com/IBM/ICDAR2021-SLP) (Apache-2.0). The online leaderboard is no longer active. Statistical rigor: single-run competition results; no error bars, confidence intervals, or multi-seed averages are reported. Rankings should be interpreted with caution given the small score differences between top teams (e.g., rank 1 vs. rank 2 differ by 0.04 TEDS overall). ### Hardware Not reported. Individual teams used V100-class GPUs; no aggregate compute figures available. ## BibTeX ``` ```bibtex @inproceedings{yepes2021icdar, title={ICDAR 2021 Competition on Scientific Literature Parsing}, author={Jimeno Yepes, Antonio and Zhong, Peter and Burdick, Douglas}, booktitle={Document Analysis and Recognition -- ICDAR 2021}, year={2021}, publisher={Springer}, doi={10.1007/978-3-030-86337-1_40} } ``` ```