Table_Dataset / README.md
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CompTSRBench: 8K train / 2K test
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metadata
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

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.