---
title: 'TableBank-Word: Weakly Supervised Table Detection'
url: https://www.emergentmind.com/topics/tablebank-word
type: topic
---

# TableBank-Word: Weakly Supervised Table Detection

TableBank-Word is the Word-derived subset of TableBank, an image-based corpus for table detection and table structure recognition constructed with weak supervision from `.docx` documents. In the original TableBank release, the Word subset contains **163,417** labeled tables for detection and **56,866** structure-recognition instances; later work also treats the Word partition as a benchmark for **single-class table detection** with **bounding boxes**, including semi-supervised Deformable-DETR experiments and active-learning studies [1903.01949][2405.04971][2509.20003].

## 1. Corpus definition and scope

Within the full TableBank corpus, the Word subset is the portion built from crawled **`.docx` Word documents**. The original dataset paper reports **417,234** labeled tables for detection across Word and LaTeX, with the Word share equal to **163,417**; for table structure recognition, the combined total is **145,463**, of which the Word share is **56,866** [1903.01949]. The dataset therefore supports two distinct tasks: **table detection**, where the output is page-level table rectangles, and **table structure recognition**, where the output is an HTML-like structural sequence.

Later papers reuse the same subset under different experimental protocols, so the reported counts are not always directly comparable. The 2024 semi-supervised detector paper treats **TableBank-Word** as the Word partition with **163,417 images**, while the 2025 active-learning paper reports a dedicated split of **73,383** training images, **2,735** validation images, and **2,281** test images for TableBank-Word [2405.04971][2509.20003]. This difference reflects distinct task setups and split definitions rather than a single canonical count.

| Source | Quantity | Meaning |
|---|---:|---|
| TableBank (2019) | 163,417 | Word detection annotations |
| TableBank (2019) | 56,866 | Word structure-recognition instances |
| Semi-supervised detection (2024) | 163,417 | Word partition used in detection experiments |
| Active learning (2025) | 73,383 / 2,735 / 2,281 | Train / validation / test images |

The original paper does **not** explicitly report the number of Word source documents or the number of Word page images overall. It reports counts of labeled tables for detection and sampled evaluation sets for detection and recognition instead.

## 2. Weak-supervision pipeline for Word documents

The Word subset is built by **crawling Word documents from the internet** and using only **`.docx`** files, because `.docx` files can be edited internally through **Office XML** [1903.01949]. In the decompressed folder, the relevant content resides in **`document.xml`**, where table regions are represented by markup between **`<w:tbl>`** and **`</w:tbl>`**. These native structural tags provide the basis for weak supervision.

For table detection, the construction pipeline is explicitly document-centric. The `.docx` file is decompressed; the XML snippets corresponding to tables are modified so that table borders become a **distinguishable color** relative to the rest of the document; the example in the paper uses a **green bounding box**; the modified document is rendered to PDF; and only **PDF pages that contain at least one table** are retained. Ground-truth boxes are then recovered by comparing the **original page** and the **annotated page** at the **pixel level**. The differing pixels reveal the inserted table borders, from which the annotation extractor recovers the **upper-left point**, the **width**, and the **height**.

The original paper also describes an alignment step: for each annotated PDF page, it adds the bounding boxes to the tables in the original page **with white color** so that the two pages align in the same position. This makes the pixel-level comparison operationally stable for extracting the rectangular box coordinates.

The reported annotation quality is high but not perfect. A manual sample of **1,000 examples** contained only **5 incorrectly labeled**, corresponding to **0.5% incorrect** and **99.5% correct** in that spot check [1903.01949]. The paper also illustrates failure modes, including a **missed** table, a **mixed** case where two tables are annotated as one, an **incomplete** annotation, and **overlapped** annotations. Those examples establish that TableBank-Word is weakly supervised rather than manually exhaustively curated.

For table structure recognition, the labeling mechanism differs. The target is an **HTML tag sequence** representing the arrangement of rows and columns and cell types, and for Word documents the labels are created by **transforming the original XML information from documents into an HTML tag sequence**. The output vocabulary contains **12 structure tokens**: `<tabular>`, `</tabular>`, `<thead>`, `</thead>`, `<tbody>`, `</tbody>`, `<tr>`, `</tr>`, `<td>`, `</td>`, `<cell_y>`, and `<cell_n>`. The semantic distinction is that **`<cell_y>`** denotes a cell with content and **`<cell_n>`** a cell without content.

## 3. Tasks, splits, and evaluation protocols

In the original TableBank formulation, **table detection** takes a page image from a Word document and predicts table bounding boxes on the page, while **table structure recognition** takes a cropped table image and predicts an HTML-like structure sequence [1903.01949]. Detection annotations are rectangular boxes; recognition annotations are sequence labels derived from Word XML.

The original Word detection evaluation protocol samples **2,000 document images** from Word documents, split into **1,000 validation** and **1,000 test** images, with each image containing **at least one table**. Recognition evaluation samples **500 tables for validation** and **500 tables for testing** from Word documents. For detection, the paper uses area-based **precision**, **recall**, and **F1 Score**:
$$
Precision=\frac{\text{Area of Ground truth regions in Detected regions}}{\text{Area of all Detected table regions}}
$$
$$
Recall=\frac{\text{Area of Ground truth regions in Detected regions}}{\text{Area of all Ground truth table regions}}
$$
$$
F1\ Score=\frac{2 \times Precision \times Recall}{Precision + Recall}
$$
For recognition, the reported metric is **4-gram BLEU** with a single reference.

Later studies adopt different evaluation protocols. The 2024 semi-supervised detector paper evaluates **table detection only**, not structure recognition, and frames the outputs as single-class object-detection **bounding boxes**. It reports **mAP**, **AP\(^ {50}\)**, **AP\(^ {75}\)**, and **AR\(_L\)**, and performs semi-supervised training with **10%**, **30%**, and **50%** of the data labeled and the remaining data treated as unlabeled; it does **not** report a conventional train/validation/test split for TableBank-Word [2405.04971]. The 2025 active-learning paper likewise uses TableBank-Word for **single-class table detection** with **bounding boxes**, and reports **mAP** at annotation budgets of **2k**, **4k**, **6k**, **8k**, and **10k** on its **73,383 / 2,735 / 2,281** train/validation/test split [2509.20003].

This multiplicity of protocols means that “TableBank-Word” names a stable data source but not a single universal evaluation regime. A plausible implication is that numerical results are directly comparable only within a paper’s own split and metric conventions.

## 4. Visual domain characteristics and relation to TableBank-LaTeX

TableBank-Word is explicitly differentiated from the LaTeX portion of TableBank by source, language coverage, and visual regularity. The Word subset is crawled from the internet, uses **`.docx`** files, and includes **English**, **Chinese**, **Japanese**, **Arabic**, and other languages because the authors **do not filter document language** [1903.01949]. In contrast, the LaTeX subset comes mainly from **arXiv**, spans source code from **2014 to 2018**, and is mainly English.

Later work sharpens the domain characterization. The 2025 active-learning paper describes TableBank-Word as a subset of images from **Microsoft Word documents**, including **business reports, forms, and various document types**, and states that its tables have **more diverse and irregular table layouts**, **varied formatting styles, fonts, and colors**, **complex table structures with merged cells and nested elements**, and **greater variability in table appearance due to user-defined formatting** [2509.20003]. It contrasts this with TableBank-LaTeX, whose tables are described as more structured, grid-like, consistent, and uniform.

The original TableBank baselines quantify the domain gap. For table detection on the Word test set, the best same-domain result is the **Word-trained ResNeXt-152** with **P 0.9530**, **R 0.8829**, and **F1 0.9166**; the best LaTeX-trained model evaluated on Word reaches **F1 0.8863** [1903.01949]. For structure recognition on Word, the **Word-trained** image-to-text model reaches **BLEU 0.7507**, whereas the **LaTeX-trained** model reaches **0.4048**. These results support the paper’s conclusion that models perform well on the **same domain** and that tables from different document types have **different visual appearance**.

The cross-domain behavior also indicates that mixed training improves generalization without necessarily maximizing Word-only accuracy. On Word detection, the best mixed **Word+LaTeX-trained** model reaches **F1 0.9067**, which is below the **0.9166** of the Word-trained model; on Word structure recognition, the mixed model reaches **0.7121**, below the Word-trained model’s **0.7507** [1903.01949]. This suggests that TableBank-Word is not merely a smaller variant of TableBank-LaTeX but a visually and statistically distinct operating domain.

## 5. Semi-supervised table detection on TableBank-Word

In the 2024 paper, TableBank-Word is a principal benchmark for **semi-supervised table detection in document images** [2405.04971]. The method is an **end-to-end semi-supervised deformable-DETR-style detector** built around two query sets,
$$
Q=\left\{q_{1}, q_{2}, \cdots, q_{n} \right\}
$$
and
$$
\hat{Q}=\left\{ \hat{q}_{1}, \hat{q}_{2}, \cdots, \hat{q}_{T} \right\},
$$
corresponding respectively to **one-to-one** and **one-to-many** assignment branches. The stated motivation is that CNN-based semi-supervised detectors rely on anchors and NMS, while transformer-based DETR-style semi-supervised methods suffer from strict **one-to-one assignment** that can yield weak or noisy pseudo-label supervision, especially early in training.

The detector uses a **student** and a **teacher**. The teacher processes only **unlabeled images** under **weak augmentation** and produces pseudo-labels; the student processes both **labeled and unlabeled images**, uses **strong augmentation** for unlabeled images, learns from labeled data and teacher-generated pseudo-labels, and the teacher is updated by **EMA** from the student. The paper conceptually defines labeled and unlabeled sets as
$$
D_{l} = \left\{ x^{l}_{i}, y^{l}_{i} \right\}^{N_{l}}_{i=1}
$$
and
$$
D_{u} = \left\{ x^{u}_{i} \right\}^{N_{u}}_{i=1}.
$$
Pseudo-labels are filtered by a confidence threshold of **0.7**.

For TableBank-Word, the reported implementation details are: **ResNet-50** backbone pre-trained on **ImageNet**; **Deformable DETR-based** detector; **10%**, **30%**, and **50%** labeled ratios; **150 epochs** of training; learning rate reduced by a factor of **0.1** at epoch **140**; strong augmentations consisting of **horizontal flips, resizing, patch removal, cropping, grayscale, Gaussian blur**; weak augmentation consisting of **horizontal flipping**; **30** one-to-one queries; **400** one-to-many queries; ground-truth replication factor **6**; loss weights **\(\alpha_1=2\)** for classification and **\(\alpha_2=5\)** for box regression; input image size **600 pixels** in the efficient setup and **800 pixels** for comparison with prior work; and **one-to-one branch only** at inference, preserving **NMS-free** DETR behavior.

The title refers to **“modulated object queries,”** but the paper does **not** provide a standalone mathematical modulation mechanism such as an explicit equation of the form \(q' = f(q, b)\). What is concretely specified is the dual-query, dual-assignment training design. The strongest technically grounded reading is therefore that the “modulated” aspect is operationalized through **dual query assignment and filtering**, while the main novelty lies in the **hybrid matching strategy**.

The TableBank-Word results are reported partition-specifically in **Table 3**. With **10% labeled** data, the method reaches **mAP 84.1**, **AP50 87.1**, **AP75 85.5**, and **AR\(_L\) 96.2**. With **30% labeled** data, it reaches **mAP 95.7**, **AP50 96.9**, **AP75 96.2**, and **AR\(_L\) 98.1**. With **50% labeled** data, it reaches **mAP 95.5**, **AP50 98.5**, **AP75 97.2**, and **AR\(_L\) 98.7**. The best **mAP** is therefore **95.7** at **30% labeled**, whereas the best **AP50** is **98.5** at **50% labeled**. Within TableBank-Word, the change from **10%** to **30%** labeled yields a **+11.6** mAP increase, while **30%** to **50%** yields a **-0.2** change in mAP and a **+1.6** increase in AP50.

The paper argues more broadly that its method surpasses prior supervised and semi-supervised methods on TableBank benchmarks, but its comparison tables are not cleanly resolved to the Word partition. The most direct Word-specific conclusion is therefore limited to the **95.7 mAP** result at **30% labeled** data and the accompanying AP50, AP75, and AR\(_L\) values.

## 6. Active learning on TableBank-Word

The 2025 paper studies **active learning** for **single-class table detection** on TableBank-Word using **CascadeTabNet** and **YOLOv9** [2509.20003]. The setup uses the split **73,383** train, **2,735** validation, **2,281** test. The paper emphasizes that the single-class nature of the problem changes the informativeness of conventional uncertainty measures, and positions TableBank-Word as the harder domain because Word-generated tables are less regular than LaTeX-generated ones.

Four selection strategies are evaluated in addition to a **Random** baseline: **Prediction Uncertainty**, **Mask Ambiguity**, **Bounding-Box Ambiguity**, and **Table Count**. The active-learning loop begins with an initial labeled set, trains a detector, performs inference on the unlabeled pool, scores or filters unlabeled examples, annotates selected samples, **continues from the previous state**, and evaluates. The reported hyperparameters specific to TableBank-Word are sparse: the **uncertainty threshold** is **0.95**, and the IoU threshold for the **Bounding-Box Ambiguity** score on TableBank-Word is **\(T_{\text{IoU}} = 0.006\)**, chosen “to ensure a sufficiently large sample set under budget constraints.”

The methods are defined operationally. **Prediction Uncertainty** bins images by confidence and prioritizes lower-confidence bins; for images with multiple predicted tables, the image-level score is the **average confidence across predicted tables**. **Bounding-Box Ambiguity** targets images with multiple overlapping predicted boxes. **Mask Ambiguity** uses the score **\(1 - \text{IoU}\)** between a detection mask and a segmentation mask, computed over all bounding-box-occupied and segmentation-mask-occupied regions in the page. **Table Count** prioritizes images containing **more than one table**, giving higher sampling probability proportionate to the number of tables. The paper also notes that some printed formulas are **corrupted**, especially the confidence-rate expression and an entropy-style uncertainty expression, so only the qualitative selection logic is unambiguous.

For **CascadeTabNet** on TableBank-Word, the reported mAP values at budgets **2k / 4k / 6k / 8k / 10k** are: **Random** 82.2 / 85.1 / 86.8 / 87.8 / 88.8; **Prediction Uncertainty** 83.1 / 87.3 / 88.1 / 89.4 / 89.8; **Mask Ambiguity** 52.1 / 86.6 / 88.4 / 89.3 / 89.5; **Bounding-Box Ambiguity** 69.7 / 84.2 / 87.4 / 88.9 / 90.3; **Table Count** 80.3 / 85.5 / 86.9 / 88.6 / 89.3. The strongest CascadeTabNet result is therefore **90.3 mAP at 10k** using **Bounding-Box Ambiguity**. The paper also highlights that **Prediction Uncertainty** reaches **89.4 mAP at 8k**, which it interprets as a **20% reduction in annotation** relative to random sampling.

For **YOLOv9** on TableBank-Word, the reported mAP values are: **Random** 64.6 / 84.2 / 88.0 / 89.6 / 88.6; **Prediction Uncertainty** 67.3 / 86.3 / 89.2 / 89.2 / 90.1; **Mask Ambiguity** 66.4 / 84.6 / 89.4 / 89.6 / 90.5; **Bounding-Box Ambiguity** 68.2 / 87.9 / 90.2 / 91.7 / 90.2; **Table Count** 67.0 / 89.3 / 90.7 / 90.3 / 91.0. The strongest mid-budget result is **91.7 mAP at 8k** using **Bounding-Box Ambiguity**, while the best **10k** result is **91.0** using **Table Count**. The paper explicitly concludes that “Sampling based on table count provides a more significant performance boost for YOLOv9, whereas ambiguity-based sampling methods perform better with CascadeTabNet.”

Several caveats accompany these results. The paper does **not** provide a numeric full-supervision baseline for TableBank-Word, does **not** specify optimizer, learning rate, batch size, augmentation policy, image size, exact mAP protocol, or seed averaging, and does **not** fully define the active-learning round design. It also reports unstable low-budget behavior for some heuristics, especially **Mask Ambiguity** at **2k** for CascadeTabNet (**52.1 mAP**) and **Bounding-Box Ambiguity** at **2k** for CascadeTabNet (**69.7**). These omissions and instabilities limit strict reproducibility, but the budgeted comparisons against **Random** remain explicit.

The active-learning study therefore positions TableBank-Word as a challenging, high-variability benchmark where careful sample selection can improve **mAP** under a fixed annotation budget. The semi-supervised study positions it as a benchmark where improved pseudo-label assignment can yield **95.7 mAP** with **30% labeled** data. Taken together, these results indicate that TableBank-Word functions not only as a source of weakly supervised labels from Word documents, but also as a stress test for annotation-efficient table detection methods under substantial visual heterogeneity.

Source: https://www.emergentmind.com/topics/tablebank-word