# doc-analysis/TableBank

TableBank: A Benchmark Dataset for Table Detection and Recognition

Repository: https://github.com/doc-analysis/TableBank
Canonical: https://ross.abutalabs.com/products/tablebank
License: Apache-2.0
License Family: permissive
Last push: 2024-08-12T04:17:40+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2739, "days_push": 751, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1084, forks 146 (observed 2026-08-28T04:03:31.373556+00:00)

## What it is
TableBank is a large image-based benchmark dataset for table detection and table structure recognition, containing 417K high-quality labeled tables extracted from Word and LaTeX documents via weak supervision. It includes trained detection and recognition models built with Detectron2 and OpenNMT, and is distributed via HuggingFace.

## Use cases
- train a model to detect tables in document images
- benchmark table structure recognition models
- extract tables from PDFs and research papers
- get labeled training data for document layout analysis
- evaluate table extraction accuracy on a standard dataset
- fine-tune object detection models for table regions

## When to choose
- you need large-scale labeled data for table detection or recognition research
- you want a standard benchmark to compare table extraction models
- you are building document layout analysis pipelines involving tables

## When to avoid
- you need production-ready table extraction software rather than a research dataset
- your use case is commercial, since the data is research-only
- you need human-annotated rather than weakly supervised labels

## Facets
- artifact type: dataset
- maturity: maintenance
- function: machine-learning, computer-vision, ocr, data-science
- domain: machine-learning, computer-vision, pdf
- platform: python, cross-platform
- tags: table-detection, table-structure-recognition, benchmark-dataset, weak-supervision, document-layout-analysis, computer-vision-dataset, natural-language-processing

## Member repositories
- doc-analysis/TableBank (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:31.373556+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:50:01.689191+00:00, confidence not recorded.
  - readme: https://github.com/doc-analysis/TableBank (fetched 2026-08-28T04:03:31.373556+00:00, sha ec7576fa0cd2)
- Data as of 2026-08-30T08:39:29.467469+00:00.