---
license: other
license_name: research-only
license_link: LICENSE
task_categories:
  - image-to-text
language:
  - zh
  - en
tags:
  - Table-Parsing
  - Table-Recognition
  - OCR
pretty_name: TableVerse-5K
size_categories:
  - 1K<n<10K
---

# TableVerse-5K <!-- omit in toc -->

**A Table-Parsing Benchmark for the StrucTab Framework**

<p align="center">
  <a href="https://github.com/VirtualLUOUCAS/StrucTab">GitHub Repo</a> •
  <a href="https://modelscope.cn/datasets/pspdada/TableVerse-5K">ModelScope Dataset</a> •
  <a href="https://arxiv.org/abs/2606.29905">Paper</a>
</p>

## Overview <!-- omit in toc -->

**TableVerse-5K** is the evaluation benchmark for **StrucTab**, a structured optimization framework for **table parsing**, the task of converting a table image into structured HTML. Each sample pairs a table image with an instruction prompt and a ground-truth HTML table, and models are scored with the TEDS / TEDS-S metrics.

<table align="center">
    <p align="center">
      <img src="docs/figures/introduction.jpg" width="80%" />
    </p>
</table>

The benchmark pipeline is illustrated below:

<table align="center">
    <p align="center">
      <img src="docs/figures/benchmark_pipeline.jpg" width="80%" />
    </p>
</table>

## Contents <!-- omit in toc -->

- [Statistics](#statistics)
- [Dataset Structure](#dataset-structure)
- [Data Format](#data-format)
- [Usage](#usage)
- [Citation](#citation)
- [License](#license)

## Statistics

| Item            | Details                                       |
| --------------- | --------------------------------------------- |
| Samples         | 5K table images                               |
| Task            | Table parsing (image → HTML table)            |
| Languages       | Bilingual (Chinese and English table content) |
| Output format   | HTML (`<table>...</table>`)                   |
| Scoring metrics | TEDS, TEDS-S                                  |

## Dataset Structure

```
data/
├── TableVerse_5K.jsonl     # annotations for all samples
└── images/                 # table images (*.jpg)
```

## Data Format

Each line of `TableVerse_5K.jsonl` is a JSON object:

```json
{
  "image_path": "images/xxx.jpg",
  "question": "You are an AI specialized in recognizing and extracting table from images...",
  "ref_answer": "<table>...</table>"
}
```

| Field        | Type   | Description                                                       |
| ------------ | ------ | ----------------------------------------------------------------- |
| `image_path` | string | Relative path from `data/`; also serves as the unique sample key  |
| `question`   | string | The instruction / prompt fed to the model together with the image |
| `ref_answer` | string | Ground-truth table in HTML (`<table>...</table>`)                 |

## Usage

Please refer to the [GitHub repository](https://github.com/VirtualLUOUCAS/StrucTab) for
the full inference and evaluation scripts.

```bash
# 1. Clone the code repository
git clone https://github.com/VirtualLUOUCAS/StrucTab
cd StrucTab/benchmark
pip install -r requirements.txt

# 2. Clone this dataset and place its contents under benchmark/data/
#    so that you have benchmark/data/TableVerse_5K.jsonl and benchmark/data/images/

# 3. Inference
python infer.py --api_type openai_compat --model_name <model> --base_url <url>

# 4. Score (requires the TEDS judging service, see the repo README)
python judge.py
```

## Citation

If you find TableVerse-5K useful, please consider citing (placeholder; to be updated):

```bibtex
TBD
```

## License

This dataset is released for **research purposes only**.