TableVerse-5K

A Table-Parsing Benchmark for the StrucTab Framework

GitHub Repo • ModelScope Dataset • Paper

Overview

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.

The benchmark pipeline is illustrated below:

Contents

• Statistics
• Dataset Structure
• Data Format
• Usage
• Citation
• 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:

{
  "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 for the full inference and evaluation scripts.

# 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):

@article{StrucTab_2026,
  title   = {{StrucTab}: A Structured Optimization Framework for Table Parsing},
  author  = {Li, Gengluo and Peng, Shangpin and Zhang, Chengquan and Wu, Binghong and Feng, Hao and Wang, Weinong and Lyu, Pengyuan and Shen, Huawen and Wan, Xingyu and Tian, Zhuotao and Hu, Han and Ma, Can and Zhou, Yu},
  journal = {arXiv preprint arXiv:2606.29905},
  year    = {2026}
}

License

This dataset is released for research purposes only.