---
title: 'PubTables-1M: Table Extraction Benchmark'
url: https://www.emergentmind.com/topics/pubtables-1m
type: topic
---

# PubTables-1M: Table Extraction Benchmark

PubTables-1M is a large-scale dataset for table extraction from unstructured documents, introduced to provide comprehensive and less ambiguous supervision for scientific-document tables. It was developed from PubMed Central Open Access (PMCOA) articles and defines table extraction as comprising table detection, table structure recognition, and functional analysis. The dataset contains nearly one million tables, is grounded in PDF coordinates rather than only raster images, and was designed to address limitations of earlier datasets, especially incomplete spatial annotation, weak cell-location labels, missing header semantics, and markup-derived inconsistency caused by oversegmentation [2110.00061].

## 1. Definition and scope

PubTables-1M supports three subtasks. **Table detection (TD)** locates tables on a page. **Table structure recognition (TSR)** recovers rows, columns, and cells, including spanning cells. **Functional analysis (FA)** identifies semantic roles, especially headers versus data regions [2110.00061].

The dataset is built from PMCOA scientific articles, where each article is available both as a PDF and as an XML document containing HTML table markup. This pairing is central: the XML provides semantic table structure, while the PDF provides visual presentation. Because the annotations are anchored in PDF coordinates, PubTables-1M supports multiple input modalities, including image-based, PDF/text-based, and potentially multimodal approaches [2110.00061].

In scale, the dataset reports **947,642 tables** for TSR. For table detection, it includes fully annotated pages containing tables, with **460,589** training pages, **57,591** validation pages, and **57,125** test pages. For TSR, the document-level split is **758,849** training tables, **94,959** validation tables, and **93,834** test tables, using an **80/10/10** split at the document level to avoid leakage across tables from the same paper [2110.00061].

The dataset is explicitly table-centric rather than page-contextualized. Later work has treated this as a strength for cropped-table TSR and as a limitation for unified page understanding. For example, CTE reuses PubTables-1M as a table-annotation backbone and embeds it into full-page context by merging it with PubLayNet, while POTATR is positioned as a later extension of the PubTables/TATR line toward page-level extraction on PubTables-v2 rather than on PubTables-1M itself [2302.01451] [2606.09788].

## 2. Annotation design and represented structure

PubTables-1M was designed to be more comprehensive than earlier large-scale table datasets. Its annotations include table bounding boxes, explicit row and column bounding boxes, cell topology, spanning cells, full cell locations including blank cells, and header information such as column headers and projected row headers [2110.00061].

The dataset distinguishes between two cell-localization concepts. A **text cell bounding box** is the union of character boxes for the text in a cell. A **grid cell bounding box** is the full geometric cell region derived from row and column intersections. This distinction matters because blank cells have no text extent but still have valid grid-cell geometry. PubTables-1M therefore supports supervision not only for cell content and topology but also for whitespace-sensitive cell geometry and blank-cell prediction [2110.00061].

For joint TSR and FA, the dataset is used with six object classes:

| Class |
|---|
| table |
| table column |
| table row |
| table column header |
| table projected row header |
| table spanning cell |

An implicit seventh class, **table grid cell**, is formed by the intersection of rows and columns [2110.00061].

The paper also defines **projected row headers (PRHs)** as rows in the body area that act as section labels spanning across the table’s logical structure. The rule used is: for each row, if the row is not in the column header and has exactly one non-blank cell that occupies the first column, then label it a projected row header [2110.00061].

Later datasets and models preserve and reinterpret these annotations. CTE states that it inherits from PubTables-1M the classes **row**, **column**, **table header**, **projected header**, **table cell**, and **grid cell**, then converts those table-region annotations into token classes and token links for graph-ready page representations [2302.01451]. This suggests that PubTables-1M’s annotation granularity has been sufficiently rich to support not only object detection formulations but also token/link reformulations.

## 3. Construction pipeline and PDF-grounded alignment

The annotation pipeline begins from PMCOA XML and PDF pairs. XML markup encodes semantic table structure but does not directly identify the corresponding PDF coordinates. To recover that correspondence, the authors process each PDF into a sequence of characters with bounding boxes, extract the character sequence from the HTML table markup, and align the two sequences with the **Needleman–Wunsch algorithm** [2110.00061].

After alignment, each text-containing cell is assigned a **text cell bounding box** defined as the union of the matched character boxes. Higher-level annotations are then inferred geometrically. The **table bounding box** is defined as the union of all text cell bounding boxes. A **row bounding box** spans the full table width, with vertical extent determined by the union of text cells for all cells whose starting row or ending row is that row. A **column bounding box** spans the full table height, with horizontal extent determined by the union of text cells for all cells whose starting column or ending column is that column [2110.00061].

The **grid cell bounding box** is then defined as the union of the bounding boxes of its rows intersected with the union of the bounding boxes of its columns. This construction yields a full geometric region even for blank cells [2110.00061].

This PDF-grounded design has downstream consequences. The original paper emphasizes that multiple modalities can be derived from the annotated PDF data [2110.00061]. Later work confirms the practical value of this grounding. CTE uses PubTables-1M annotations together with text extracted from PDFs using **PyMuPDF**, assigning token labels according to enclosing annotated regions and creating links among tokens belonging to the same row, column, or grid cell [2302.01451]. A plausible implication is that the original PDF-coordinate anchoring of PubTables-1M made such later reinterpretations substantially easier.

## 4. Canonicalization and the oversegmentation problem

A central contribution of PubTables-1M is its treatment of **oversegmentation**, defined as the case where a cell that should logically be a single spanning header cell is split into multiple smaller grid cells in the annotation. The paper argues that this creates contradictory supervision during training and underestimates model performance during evaluation, especially for complex tables with hierarchical headers [2110.00061].

The canonicalization procedure is intended to ensure that each table has a **unique, unambiguous structure interpretation**. The paper motivates this procedure with several assumptions: consistency with the Wang model of tabular structure, unique indexing by keys, a requirement that internal nodes have at least two children, and an assumption that oversegmentation preserves visual appearance [2110.00061].

The algorithm first extends header annotations. It splits blank spanning cells into blank grid cells, may add the first row to the column header if the first row starts with a blank cell, may iteratively extend the column header until every column in the column header has at least one complete cell spanning only that column, labels projected row headers using the one-non-blank-cell-in-first-column rule, and may extend the row-header region when cells in the first column below the column header are spanning or blank [2110.00061].

It then performs recursive merges under specific conditions: column-header cells are merged with adjacent cells above or below that span the exact same columns; blank cells may be merged upward or downward under specified all-blank conditions; projected row headers are merged across the full row; and row-header cells may be recursively merged with adjacent blank cells below [2110.00061].

The paper reports that canonicalization modifies **34.7% of all tables**, or **65.8% of complex tables**. It also states that **52.7%** of tables are complex after canonicalization, compared with **40.1%** before canonicalization [2110.00061].

Evidence for reduced ambiguity is given through projected row header oversegmentation statistics. Counting a PRH as oversegmented if its row contains a blank cell, the paper reports **0%** PRH oversegmentation for PubTables-1M, compared with **15.79%** for SciTSR, **58.65%** for PubTabNet, and **98.87%** for FinTabNet [2110.00061]. This is not presented as a complete measure of all oversegmentation, but it supports the claim that the dataset removes a major source of annotation inconsistency.

## 5. Quality control, evaluation methodology, and baseline results

Because manual review at PubTables-1M scale is infeasible, the dataset is filtered automatically. Tables are discarded if rendered markup yields overlapping rows or columns. For each cell, the paper compares original XML text with PDF text extracted from the corresponding grid-cell box and discards tables whose average normalized edit distance exceeds **0.05**. For each word inside the table boundary, it computes overlap with the most-overlapping grid cell and discards tables whose average is below **0.9**. It also removes tables with more than **100 objects**, with **less than 0.1%** of tables discarded as outliers [2110.00061].

Evaluation spans both object-detection metrics and table-structure metrics. For TD and joint TSR+FA object detection, the paper uses COCO-style **AP**, **AP\(_{50}\)**, **AP\(_{75}\)**, and **AR** [2110.00061]. For TSR, it reports **table content accuracy**, **adjacent-cell content F-score**, and **GriTS** variants. The paper gives the GriTS formula as
\[
\mathrm{GriTS}_f(\mathbf{A}, \mathbf{B}) = \frac{2 \cdot \sum_{i,j} f(\tilde{\mathbf{A}_{i,j}, \tilde{\mathbf{B}_{i,j})} {|\mathbf{A}| + |\mathbf{B}|}
\]
with variants \(\mathrm{GriTS}_{\mathrm{Top}}\), \(\mathrm{GriTS}_{\mathrm{Cont}}\), and \(\mathrm{GriTS}_{\mathrm{Loc}}\) for topology, content, and location recognition respectively [2110.00061].

The paper evaluates standard object-detection architectures without task-specific customization: **DETR** and **Faster R-CNN** [2110.00061]. For TD, DETR achieves **AP = 0.966**, **AP50 = 0.995**, **AP75 = 0.988**, **AR = 0.981**, while Faster R-CNN achieves **AP = 0.825**, **AP50 = 0.985**, **AP75 = 0.927**, **AR = 0.866** [2110.00061].

For joint TSR + FA object detection, DETR achieves **AP = 0.912**, **AP50 = 0.971**, **AP75 = 0.948**, **AR = 0.942**, compared with Faster R-CNN at **AP = 0.722**, **AP50 = 0.815**, **AP75 = 0.785**, **AR = 0.762** [2110.00061].

For TSR evaluation on canonical data, DETR trained and tested on canonical labels reaches, for all tables, **\(Acc_{Cont} = 0.8138\)**, **\(GriTS_{Top} = 0.9845\)**, **\(GriTS_{Cont} = 0.9846\)**, **\(GriTS_{Loc} = 0.9781\)**, and **\(Adj_{Cont} = 0.9774\)**. On complex tables, the same model reaches **\(Acc_{Cont} = 0.6944\)** and **\(GriTS_{Top} = 0.9752\)** [2110.00061].

The canonicalization ablation is among the dataset’s most consequential empirical findings. DETR trained and tested on non-canonical data achieves **\(Acc_{Cont} = 0.7336\)** and **\(GriTS_{Top} = 0.9762\)** for all tables, while the same model evaluated on canonical test data drops to **\(Acc_{Cont} = 0.5851\)** and **\(GriTS_{Top} = 0.9576\)** [2110.00061]. The paper interprets this as showing both better supervision and more reliable evaluation from canonical labels.

## 6. Role in later research and benchmark evolution

PubTables-1M has continued to function as both a benchmark and a source of supervision in later work. In "Latent Diffusion for Guided Document Table Generation," PubTables-1M is used as the primary real dataset for training a synthetic table-image generator, as the source of row and column annotations used to create conditioning masks, and as the benchmark for testing whether those synthetic images are useful for downstream row/column structure recognition [2408.09800].

That work states that PubTables-1M contains **575,305 annotated document pages for table detection** and **947,642 fully annotated table crops for structure recognition and functional analysis**, and uses the split of **758,849** training, **93,834** testing, and **94,959** validation table images for structure-oriented experiments [2408.09800]. It trains a latent diffusion model on **450k samples** from the PubTables dataset, generates **250k** synthetic annotated table images, and trains **YOLOv5** only on those synthetic images. Evaluated on the PubTables-1M test set of **93,834** images, that synthetic-only model reaches **\(mAP_{50} = 0.947\)** and **\(mAP_{75} = 0.918\)**, compared with **\(0.970\)** and **\(0.944\)** for YOLOv5 trained on real PubTables data [2408.09800]. The paper frames this as evidence that PubTables-1M supervision can be converted into useful synthetic, pre-annotated training data.

FastTab uses PubTables-1M as a direct benchmark for single-table or tightly cropped table TSR. It follows the official split of **758,849** training, **94,959** validation, and **93,834** test tables, and evaluates only **GriTS\(_\text{Top}\)** on PubTables-1M [2605.22422]. In the reported comparison, **FastTab** achieves **98.27** GriTS\(_\text{Top}\) at **39.50 FPS**, compared with **86.16** for Faster R-CNN, **98.45** for DETR, **97.62** for DETR-NC, and **99.22** for VAST [2605.22422]. This positions PubTables-1M as a continuing reference benchmark for topology-oriented TSR.

CTE recontextualizes PubTables-1M by merging a filtered overlapping subset with PubLayNet. In that setting, PubTables-1M contributes table-specific supervision, including row, column, header, projected header, table cell, and grid cell signals, which are then projected into token labels and token links for the task of Contextualized Table Extraction [2302.01451]. CTE is much smaller, at **75k pages** and **35k tables**, because it keeps only overlap with PubLayNet and discards misaligned or incomplete pages [2302.01451]. This later use underscores that PubTables-1M is not merely a benchmark but also an annotation substrate for newer task formulations.

POTATR represents a different evolutionary path. It does not benchmark on PubTables-1M directly, but it is presented as extending **TATR**, the DETR-based model associated with the PubTables-1M line, from cropped-table TSR to page-level extraction on **PubTables-v2** [2606.09788]. The paper explicitly situates PubTables-1M as the earlier benchmark centered on cropped-table structure recognition, while PubTables-v2 adds page-level and full-document contexts [2606.09788]. This suggests a benchmark-family progression rather than a replacement.

## 7. Significance, limitations, and interpretive context

PubTables-1M is best characterized as both a scale contribution and a data-quality intervention. Its distinctive claims are not limited to the size of **947,642** tables, but include PDF-grounded spatial supervision, explicit row and column localization, representation of blank cells, header and projected-row-header annotation, automated quality filtering, and canonicalization intended to remove oversegmentation and enforce a unique structure interpretation [2110.00061].

Its importance for model design is evident in the strong performance of generic object detectors trained on the dataset. The original paper argues that transformer-based object detection models can achieve excellent results for TD, TSR, and FA without special customization [2110.00061]. Later research reinforces the centrality of its annotation scheme: latent diffusion methods treat PubTables-1M row and column annotations as controllable generation conditions [2408.09800]; grid-centric recognizers such as FastTab evaluate against its topology benchmark [2605.22422]; and page-level contextual datasets such as CTE inherit and reinterpret its table-annotation vocabulary [2302.01451].

Several limitations are also explicit. The dataset is derived from PMCOA scientific articles, so its construction assumptions and canonicalization heuristics are PMCOA-specific [2110.00061]. The original paper states that multi-page tables are out of scope and that full row-header inference beyond projected row headers is also outside scope [2110.00061]. Later work makes related distinctions: PubTables-1M remains table-centric, whereas CTE is page-contextualized [2302.01451], and PubTables-v2 extends the benchmark family toward page-level and document-level extraction [2606.09788].

A common misconception is to treat PubTables-1M as simply a larger markup-derived table dataset. The evidence reported in the original paper does not support that reduction. The dataset’s central premise is that better ground truth materially improves both learning and evaluation reliability, and the canonical versus non-canonical experiments are presented precisely to show that annotation quality, not merely dataset size, changes observed model performance [2110.00061]. A plausible implication is that PubTables-1M’s long-term influence comes as much from its formalization of what counts as complete and canonical table supervision as from its scale.

Source: https://www.emergentmind.com/topics/pubtables-1m