# PubTables-v2: Full-Page and Multi-Page Table Extraction

> PubTables-v2 is a large-scale dataset for table extraction covering cropped tables, single-page context, and full-document multi-page tables, including the first large benchmark for multi-page table structure recognition.

- Authors: Hunter Heidenreich
- Published: 2025-12-11
- Updated: 2026-03-26
- Paper: Preprint (arXiv) <https://arxiv.org/abs/2512.10888>
- Dataset: PubTables-v2 Dataset <https://huggingface.co/datasets/kensho/PubTables-v2>
- Code: Code (Kensho / PubTables-v2) <https://huggingface.co/datasets/kensho/PubTables-v2>
- Model: Models (TATR-v1.2-Pub, POTATR-v1.0-Pub) <https://huggingface.co/datasets/kensho/PubTables-v2>
- Source: Bevaya Labs, <https://labs.bevaya.ai/>

---


## TL;DR

PubTables-v2 is a new large-scale dataset from Kensho Technologies for table extraction from scientific documents. It introduces three collections organized by context: 136k large cropped tables, 548k single-page tables with caption and footer annotations, and 9,172 full documents with 9,492 multi-page tables spanning up to 13 pages. It is the first large benchmark for multi-page table structure recognition, and the authors also demonstrate a cross-page table continuation classifier that significantly improves document-level extraction performance.

---

## What kind of paper is this?

**Dominant:** $\Psi\_{\text{Resource}}$ (the primary contribution is the dataset itself: a large-scale, quality-controlled collection of annotated tables at multiple levels of document context, including the first large multi-page table benchmark).

**Secondary:** $\Psi\_{\text{Evaluation}}$ (the paper introduces generalized GriTS and TEDS metrics for multi-table inputs and establishes baseline performance across four distinct tasks); $\Psi\_{\text{Method}}$ (the paper introduces POTATR, a page-object adaptation of TATR, and a cross-page table continuation classifier).

---

## What is the motivation?

Table extraction (TE) has traditionally been decomposed into two sequential stages: detecting individual tables, then recognizing their internal structure from cropped images. This decomposition has known drawbacks. Multi-stage pipelines are complex to maintain, and processing tables in isolation discards potentially useful surrounding context. Interest has grown in methods that extract tables directly from full page or document images, including smaller specialized vision-language models (VLMs), but demonstrating progress on this richer problem has been difficult because no adequate benchmarks existed.

Specific gaps the authors identify:

- No large-scale dataset annotates table structure within full-page context with strict quality control and complete coverage (every table on a page must be annotated).
- No large dataset connects tables to their captions and footers with hierarchical relationships.
- No published dataset exists for multi-page table extraction, despite its clear practical importance.
- Existing benchmarks like PubTables-1M have been in use long enough that current model pre-training practices create data leakage concerns.
- Existing datasets skew toward short, narrow tables; challenging long and wide tables are underrepresented.

---

## What is the novelty?

**Three context-level collections:** Rather than splitting TD and TSR into separate subtasks, PubTables-v2 organizes its data by the amount of surrounding context available:

1. **Cropped Tables** (135,578 samples): exclusively long ($\geq 30$ rows) or wide ($\geq 12$ columns) tables, representing the challenging tail that prior datasets undersample. This collection includes 32% more long or wide tables than PubTables-1M, despite having a fraction of the total count.
2. **Single Pages** (467,541 pages; 548,414 tables): full-page annotations with bounding boxes for tables, rows, columns, spanning cells, column headers, projected row headers, captions, and footers, plus a hierarchical relation set connecting each table to its associated captions and footers.
3. **Full Documents** (9,172 documents; 137,095 pages; 24,862 tables): document-level annotations where every document contains at least one multi-page or split table. This collection includes 9,492 multi-page tables and 630 single-page split tables (split across two columns of a two-column page).

**Stricter quality control:** PubTables-v2 tightens the annotation quality thresholds from PubTables-1M. The mean normalized edit distance threshold is $\alpha = 0.02$ (down from $0.05$) and a new maximum per-cell edit distance threshold $\beta = 0.2$ is added. For the page- and document-level collections, if any table in a document fails quality control, the entire document is discarded, ensuring no page has missing table annotations.

**Generalized evaluation metrics:** Standard GriTS and TEDS metrics are defined for a single predicted table against a single ground truth table. The authors generalize both to handle a predicted set of tables against a ground truth set. Using the Hungarian algorithm to find the optimal one-to-one matching, they compute a pseudo-F1 score aggregated over all samples:

$$
\text{GriTS}\_f(\mathbf{A}, \mathbf{B}) = \frac{2 \sum\_{i,j} f(\tilde{\mathbf{A}}\_{i,j}, \tilde{\mathbf{B}}\_{i,j})}{|\mathbf{A}| + |\mathbf{B}|}
$$

where $\mathbf{A}$ and $\mathbf{B}$ are the ground truth and predicted table matrices and $f$ is a cell-level similarity function. For multi-table inputs, the true positive scores are summed across all samples before computing the final pseudo-F1.

**POTATR (Page-Object Table Transformer):** An adaptation of TATR for full-page extraction. POTATR extends TATR by adding two page-level object classes (caption, footer) and their rotated counterparts (16 classes total), doubling the object queries to 250, and adding a relation head (a three-layer MLP) to predict parent-child relationships between a table and its associated structures.

**Cross-page table continuation classifier:** A binary image classifier trained to predict whether the last table on one page continues onto the next. The input is two contiguous page images concatenated side by side. A ViT-B-16 classifier trained on PubTables-v2 achieves recall of 0.995 and precision of 0.987, suggesting strong visual cues for this task in the dataset.

---

## What experiments were performed?

The authors evaluate several recent smaller, specialized VLMs (SmolDocling-256M, GraniteDocling-258M, Qwen2.5-VL-3B, Granite-Vision-3.2-2B, DeepSeek-OCR, DeepSeek-OCR 2, dots.ocr) plus TATR-based models trained on PubTables-v2 data.

**Experiment 1: Cropped table structure recognition.** Models are evaluated on the Cropped Tables collection (long and wide tables). TATR-v1.2-Pub (fine-tuned on PubTables-v2) is compared against seven VLMs and prior TATR versions. TATR is also compared with four text sources: EasyOCR, PaddleOCR, docTR, and text extracted directly from the PDF (DT).

**Experiment 2: Page-level table extraction.** Models are evaluated on the Single Pages collection, requiring prediction of all tables on a page. POTATR-v1.0-Pub is introduced here for the first time. Metrics are the generalized GriTS and TEDS variants.

**Experiment 3: Document-level table extraction.** Models are evaluated on the Full Documents collection. Two scenarios are compared: (1) providing all pages of a document at once, and (2) processing pages individually while evaluating at the document level. Only Qwen2.5-VL-3B was able to process full documents at once due to context window constraints.

**Experiment 4: Cross-page table continuation prediction.** ResNet-50 and ViT-B-16 are trained on 15,830 pairs (9,866 positive, 5,964 negative) from the Full Documents collection and evaluated on classification metrics (recall, precision, F1, AUC).

**Experiment 5: Document-level TE with cross-page merging.** The best-performing document-level model (dots.ocr, processed page-by-page) is augmented with the ViT-B-16 continuation classifier. When the classifier predicts a table continuation and the table column counts match, tables are concatenated vertically.

**Additional experiments (appendix):** Single-page split table extraction, analysis of how often models predict the correct number of tables per page, comparison of image-to-graph architectures (Relationformer, EGTR, POTATR) in a small-scale experiment, and an ablation on training set size for the continuation classifier.

---

## What are the outcomes/conclusions?

**Key findings:**

- Among VLMs on cropped long/wide tables, dots.ocr performs best with $\text{GriTS}\_{\text{Con}} = 0.801$. TATR-v1.2-Pub with direct text extraction achieves $\text{GriTS}\_{\text{Con}} = 0.980$, roughly halving the error of the prior TATR-v1.1-Pub model, showing the value of additional training data for this challenging subset.
- At the page level, dots.ocr again leads among VLMs ($\text{GriTS}\_{\text{Con}} = 0.899$), still below POTATR with direct text extraction ($\text{GriTS}\_{\text{Con}} = 0.957$).
- At the document level (pages processed individually), the best VLM, dots.ocr, achieves only $\text{GriTS}\_{\text{Con}} = 0.577$ and perfect content-and-structure exact match on only 11.8% of tables ($\text{Acc}\_{\text{Con}} = 0.118$). The only model tested on full documents at once, Qwen2.5-VL-3B, scores $\text{GriTS}\_{\text{Con}} = 0.047$.
- The cross-page continuation classifier (ViT-B-16) achieves F1 = 0.991, indicating strong visual cues in the dataset. Even with only 250 training samples, models exceed F1 = 0.86.
- Augmenting dots.ocr with the continuation classifier improves document-level $\text{GriTS}\_{\text{Con}}$ from 0.577 to 0.684, a substantial gain from a lightweight auxiliary model.

**Failure modes observed:**

- Qwen2.5-VL-3B enters infinite repetition loops on full-document inputs, even for short two-page documents.
- Granite-Vision-3.2-2B always predicts exactly one table per page, regardless of actual table count, which artificially inflates its split-table metrics.
- No model achieves $\text{Acc}\_{\text{Con}} > 0$ on single-page split tables.

**Limitations acknowledged by the authors:**

- PubTables-v2 is sourced exclusively from English-language scientific articles (PubMed); it does not cover other domains or languages.
- The dataset is not intended to replace benchmarks covering broader variation in table and page appearance.
- Row and column bounding boxes are not annotated for multi-page tables in the Full Documents collection.

---

## Reproducibility

| Resource | Type | License | Link |
|---|---|---|---|
| Preprint | Paper | Unknown | [arXiv 2512.10888](https://arxiv.org/abs/2512.10888) |
| PubTables-v2 Dataset | Dataset | CDLA-Permissive-2.0 | [HuggingFace](https://huggingface.co/datasets/kensho/PubTables-v2) |
| Code | Code | MIT | [HuggingFace](https://huggingface.co/datasets/kensho/PubTables-v2) |
| TATR-v1.2-Pub, POTATR-v1.0-Pub | Models | MIT | [HuggingFace](https://huggingface.co/datasets/kensho/PubTables-v2) |

### Models

- **TATR-v1.2-Pub:** 28M parameters, identical architecture to TATR-v1.1-Pub, fine-tuned on the PubTables-v2 Cropped Tables collection. Weights initialized from TATR-v1.1-Pub (pre-trained on PubTables-1M cropped tables).
- **POTATR-v1.0-Pub:** 29M parameters, extends TATR with 125 additional object queries (250 total), two additional object classes (caption, footer) plus rotated counterparts (16 classes total), and a three-layer MLP relation head. Initialized from TATR-v1.1-Pub; relation head and extra queries are randomly initialized.
- **Cross-page classifiers:** Standard ResNet-50 and ViT-B-16 image classifiers. Input is two page images concatenated horizontally.
- All models will be released at the HuggingFace dataset page under MIT license.

### Algorithms

Both TATR-v1.2-Pub and POTATR-v1.0-Pub follow the default TATR training setup (AdamW optimizer, standard DETR hyperparameters) unless noted otherwise.

- **TATR-v1.2-Pub training:** 8 Nvidia T4 GPUs, batch size 2 per GPU (effective batch size 16). Epoch defined as 100,000 samples; trained for 160 epochs. Initial learning rate $5 \times 10^{-5}$, gamma 0.9 every 4 epochs. No-object class weight `eos_coef` = 0.3.
- **POTATR-v1.0-Pub training:** Same hardware setup. 100 epochs on Single Pages collection. Initial learning rate $5 \times 10^{-5}$, gamma 0.9 every 2 epochs. `eos_coef` = 0.1. Relation loss weight = 0.05.
- **Cross-page classifiers:** Trained for 200 epochs, best checkpoint selected by validation F1.

### Data

- Source: PubMed Central articles published 2023-2025 (not included in PubTables-1M).
- Annotation leverages author-supplied HTML/XML table markup aligned to PDF via sequence matching.
- Quality thresholds: mean normalized edit distance $\alpha < 0.02$, maximum per-cell edit distance $\beta < 0.2$.
- Table structure canonicalization scheme from PubTables-1M is applied, plus the improved column header annotation correction from Smock et al. (ICDAR 2023).
- Splits: train, validation, public test, and hidden test sets. The hidden test set (4.3% of cropped tables) is withheld to allow future leakage detection.
- Annotation formats: PASCAL VOC for object detection training, grid matrix format for GriTS, HTML for TEDS, JSON for cells and relations.
- Dataset license: CDLA-Permissive-2.0.

### Evaluation

- **GriTS variants:** $\text{GriTS}\_{\text{Top}}$ (cell topology), $\text{GriTS}\_{\text{Con}}$ (cell content via normalized edit distance). Aggregated as pseudo-F1 weighted by cell count across all samples.
- **TEDS and TEDS-S:** tree-edit distance similarity on HTML table representation, generalized to sets via Hungarian matching.
- **Exact match accuracy:** $\text{Acc}\_{\text{Top}}$ (topology) and $\text{Acc}\_{\text{Con}}$ (content + topology).
- **Graph metrics (POTATR):** Edge F1 at IoU threshold 0.8 for relation prediction.
- No statistical significance testing or multi-run reporting. Single training run for each model configuration.
- TEDS computation for full documents has a 10-minute timeout; affected samples are bounded $[0, 1]$.

### Hardware

- **TATR and POTATR training:** 8 Nvidia T4 GPUs. GPU-hours not reported.
- **VLM inference:** vLLM used for SmolDocling, GraniteDocling, and Qwen2.5-VL-3B. Inference hardware and latency not reported.
- Larger models (Qwen2.5-VL-7B) evaluated only qualitatively on a small number of documents due to computational cost.

---

## BibTeX

```bibtex
@article{smock2025pubtablesv2,
  title={PubTables-v2: A new large-scale dataset for full-page and multi-page table extraction},
  author={Smock, Brandon and Faucon-Morin, Valerie and Sokolov, Max and Liang, Libin and Khanam, Tayyibah and Ramesh, Amrit and Courtland, Maury},
  journal={arXiv preprint arXiv:2512.10888},
  year={2025}
}
```