Datasets:
File size: 2,208 Bytes
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license: cc-by-4.0
task_categories:
- image-to-text
language:
- en
tags:
- table-structure-recognition
- table-recognition
- html
- document-understanding
pretty_name: CompTSRBench
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: split
dtype: string
- name: pattern
dtype: int64
- name: image
dtype: image
- name: html
dtype: string
splits:
- name: train
num_examples: 8000
- name: test
num_examples: 2000
---
# CompTSRBench
CompTSRBench is a benchmark of 10,000 structurally complex synthetic tables for table structure recognition (image → HTML).
Each table pairs a rendered image with its ground-truth HTML. The tables cover 25 canonical structural patterns
(hierarchical headers, row and column spans, gridless regions and more) across application domains such as finance,
science, education and healthcare.
| Split | Tables |
|---|---|
| train | 8,000 |
| test | 2,000 |
Every pattern appears in both splits, with at least 40 test tables per pattern.
## Fields
| Field | Type | Description |
|---|---|---|
| `id` | string | Table identifier |
| `split` | string | `train` or `test` |
| `pattern` | int | Canonical structural pattern (1–25) |
| `image` | image | Rendered table image |
| `html` | string | Ground-truth table HTML (minified, no styling); headers are marked with `<th>` |
## Structural statistics (all 10K tables)
- 81.4% contain column spans, 89.5% row spans, 58.4% multi-level headers
- On average 17.3 merged cells per table
## Usage
```python
from datasets import load_dataset
ds = load_dataset("researchgroup542/Table_Dataset")
example = ds["test"][0]
example["image"] # PIL image
example["html"] # ground-truth HTML
```
## Evaluation
We report TEDS, TEDS-Struct and the Triple Metric Score (TMS), a structure-first metric that checks each cell's position,
header context and content in that order. See the paper for details.
## License
CC BY 4.0. All table content is synthetically generated and does not refer to real individuals or organizations.
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