rayhu/table-extraction-evaluation

Table Extraction Evaluation Dataset

A dataset for training and benchmarking verifier models that predict the quality of structured table extraction from documents — without access to ground truth at inference time.

Modern LLMs and vision-language models extract tables from PDFs impressively but inconsistently: structural recognition is largely solved, yet semantic content accuracy remains unreliable (e.g., TATR reaches GriTS ≈ 0.985 on PubTables-1M while content accuracy stays around 0.82). In high-stakes workflows — legal, financial, medical — pipelines need an automated way to judge whether an extraction can be trusted. This dataset provides supervision for exactly that task: each example pairs a machine-generated table extraction with its ground truth and a computed quality score, so a model can learn to predict extraction quality directly from the extracted output.

Built for the Stanford CS230 project "A Neural Verifier for Structured Table Extraction" (Ray Hu, Nofel Teldjoune, Hiva Zaad).

Dataset Summary

| | |

|---|---|

| Total examples | 39,066 |

| Train split | 36,066 |

| Test split | 3,000 |

| Format | Parquet |

| License | Apache 2.0 |

How It Was Built

1. Upstream source: SciTSR — tables from scientific journals with human-annotated ground-truth JSON structure.

2. Extraction generation: each table image was processed with Table Transformer (TATR) for structure recognition and EasyOCR for cell content, producing a machine-generated JSON extraction of the same table.

3. Quality labeling: each (ground truth, generated) pair was scored with a composite structural-similarity metric:

similarity_score = 0.50 * cell_detection_F1

                 + 0.25 * row_accuracy

                 + 0.25 * col_accuracy

The resulting scores span 0.04–1.0, reflecting the realistic range of extraction quality that production pipelines encounter.

Data Fields

| Field | Type | Description |

|---|---|---|

| id | string | Source table identifier (SciTSR document + table index) |

| split | string | train or test |

| similarity_score | float64 | Quality label in [0, 1] — the regression target |

| ground_truth | dict | Human-annotated table structure: list of cells with content, start_row, end_row, start_col, end_col, id, tex |

| generated | dict | Machine-generated extraction of the same table, same schema |

Usage

from datasets import load_dataset

ds = load_dataset("rayhu/table-extraction-evaluation")

example = ds["train"][0]

print(example["similarity_score"])   # e.g. 0.5315

print(example["ground_truth"]["cells"][0])

print(example["generated"]["cells"][0])

A typical use: serialize generated (optionally with ground_truth during training), embed it, and train a regression model to predict similarity_score. At inference, the verifier scores new extractions with no ground truth available — enabling automated QA gates, model benchmarking, and extraction-pipeline monitoring.

Baseline

A 2-layer MLP over sentence-transformers/all-mpnet-base-v2 embeddings trained on this dataset serves as the reference baseline. Training code, data-processing scripts, and the full report: github.com/rayhu/cs230-evaluation-model.

Limitations

• Quality labels are computed from a single composite metric; other aspects of extraction quality (numerical consistency, header semantics) are not separately labeled.

• Source tables come from scientific journals (SciTSR); domain shift should be expected when applying verifiers to financial, legal, or medical documents.

• Generated extractions come from one pipeline (TATR + EasyOCR); scores reflect that pipeline's error distribution.

Citation

If you use this dataset, please cite:

@misc{hu2025tableextractioneval,

  title  = {Table Extraction Evaluation Dataset},

  author = {Hu, Ray and Teldjoune, Nofel and Zaad, Hiva},

  year   = {2025},

  note   = {Stanford CS230 project: A Neural Verifier for Structured Table Extraction},

  url    = {https://huggingface.co/datasets/rayhu/table-extraction-evaluation}

}

Upstream data derived from SciTSR: Chi et al., Complicated Table Structure Recognition, arXiv:1908.04729, 2019.