StrucTab

A Structured Optimization Framework for Table Parsing

中文版 • GitHub Repo • HuggingFace Dataset • ModelScope Dataset • Paper

News

• [2026.06] 📖 Code and the TableVerse-5K benchmark are released!
• [2026.06] 🎉 Our StrucTab is accepted by ECCV 2026!

Overview

StrucTab is a structured optimization framework for table parsing, the task of converting a table image into structured HTML. Instead of treating parsing as a flat image-to-text problem, StrucTab decomposes it into three coupled subtasks, namely row/column counting, merged-cell analysis, and final HTML generation, and optimizes a reinforcement-learning reward that is itself decomposed along the same axes (validity, structure, content).

Contents

• Repository Layout
• Code
• Benchmark
• Citation
• License

Repository Layout

This repository releases:

• code/ — the training-data construction pipeline, the Uni-TabRL reward, its four dependency services, and the analysis scripts behind the paper's figures and tables.
• benchmark/ — a self-contained inference and evaluation harness for the TableVerse-5K table-parsing benchmark, scored with the structure-aware TEDS / TEDS-S metrics.

Full directory tree (click to expand)

StrucTab/
├── README.md
├── code/                         # training + RL reward + analysis (see code/README.md)
│   ├── training_data/            # build sequential-reasoning data from (image, HTML) pairs
│   ├── Uni_TabRL/
│   │   ├── reward/               # the decomposed RL reward (validity / structure / content)
│   │   ├── server/               # the four reward dependency services
│   │   │   └── TEDS_judger/      # TEDS / TEDS-S scoring service (also used by the benchmark)
│   │   └── configs/servers/      # endpoint lists for the reward services
│   └── analysis/                 # scripts behind the paper figures / tables
└── benchmark/                    # inference + evaluation harness for TableVerse-5K
    ├── apis/                     # pluggable backends: openai_compat | local_vllm
    ├── utils/                    # incremental writer, image encoding, signal handling
    ├── data/                     # ← place the TableVerse-5K dataset here
    ├── infer.py                  # entry: inference  → infer_results/<tag>/results.jsonl
    ├── judge.py                  # entry: TEDS scoring → judge_results/<tag>/results.jsonl
    ├── judger_server.json        # TEDS service endpoint list (host:port)
    └── requirements.txt

Code

The model-side code (training-data construction, the Uni-TabRL reward, the four dependency services, and the analysis scripts) is documented separately in code/README.md (中文版：code/README_zh.md).

The optimization framework is illustrated below:

Benchmark

benchmark/ is a self-contained harness for the TableVerse-5K table-parsing benchmark. It runs a two-stage pipeline, inference → judging, with pluggable API backends (openai_compat / local_vllm) and a structure-aware TEDS / TEDS-S scorer. Both stages support resume by keying every sample on its image_path.

For full setup, data download, and step-by-step inference / judging instructions, see benchmark/README.md (中文版：benchmark/README_zh.md).

The benchmark pipeline is illustrated below:

Citation

If you find StrucTab useful, please consider citing:

@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 project is released for research purposes only.