Datasets:

amazon
/

ConfBench

language:
  - en
license: cc-by-nc-4.0
task_categories:
  - document-question-answering
  - image-to-text
pretty_name: FCC Invoices Verified Augmented
size_categories:
  - 1K<n<10K
annotations_creators:
  - expert-generated
source_datasets:
  - https://indicodatasolutions.github.io/RealKIE/
  - https://huggingface.co/datasets/amazon-agi/RealKIE-FCC-Verified
tags:
  - document-understanding
  - invoice
  - ocr
  - noise-augmentation
  - confidence-calibration
  - information-extraction
  - kie
dataset_info:
  features:
    - name: id
      dtype: string
    - name: noise_variant
      dtype: string
    - name: page_count
      dtype: int32
    - name: json_response
      dtype:
        struct:
          - name: Agency
            dtype: string
          - name: Advertiser
            dtype: string
          - name: GrossTotal
            dtype: float64
          - name: PaymentTerms
            dtype: string
          - name: AgencyCommission
            dtype: float64
          - name: NetAmountDue
            dtype: float64
          - name: LineItems
            sequence:
              struct:
                - name: LineItemDescription
                  dtype: string
                - name: LineItemStartDate
                  dtype: string
                - name: LineItemEndDate
                  dtype: string
                - name: LineItemDays
                  dtype: string
                - name: LineItemRate
                  dtype: float64
  splits:
    - name: test
      num_examples: 1346

FCC Invoices Verified Augmented

Dataset Description

FCC Invoices Verified Augmented is a document understanding benchmark dataset consisting of 75 real-world Federal Communications Commission (FCC) invoice documents, each augmented with up to 18 distinct document degradation pipelines. The dataset is designed to support confidence calibration research, OCR robustness evaluation, and key information extraction (KIE) under realistic noise conditions.

Each document has:

A clean original PDF
Up to 18 noisy versions generated by distinct Augraphy-based degradation pipelines
Verified ground-truth entity annotations

Total samples: 1,346 (75 documents × up to 18 noise pipelines)

Quick Start

Metadata + ground truth only (fastest, no PDFs):

from datasets import load_dataset

ds = load_dataset("amazon/ConfBench", data_files="data/test-00000-of-00001.parquet", split="train")

for sample in ds:
    print(sample["id"], sample["noise_variant"])
    print(sample["json_response"]["Agency"])

With PDFs (downloads the full dataset locally):

import pyarrow.parquet as pq
from huggingface_hub import snapshot_download
from pathlib import Path

local_dir = Path(snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset"))

table = pq.read_table(local_dir / "data" / "test-00000-of-00001.parquet")
df = table.to_pandas()

# Access a PDF
pdf_path = local_dir / "pdfs" / df["id"][0]

# Access ground truth
gt = df["json_response"][0]
print(gt["Agency"], gt["GrossTotal"])

Or clone with git:

git clone https://huggingface.co/datasets/amazon/ConfBench

Dataset Summary

Property	Value
Domain	Legal / Broadcast Advertising
Document Type	FCC Invoice (multi-page PDF)
# Base Documents	75
# Noise Pipelines	up to 18 per document
# Total Samples	1,346
Avg Pages per Doc	~2
License	CC BY-NC 4.0

Dataset Structure

Repository Layout

amazon/ConfBench/
├── data/
│   └── test-00000-of-00001.parquet   # Metadata + ground truth (all variants)
├── pdfs/
│   ├── {doc_id}__original.pdf        # Original clean PDF
│   ├── {doc_id}__default.pdf         # Default noise variant
│   └── {doc_id}__{noise_variant}.pdf # Other noise variants

Ground Truth Schema (`json_response`)

{
  "document_class": {
    "type": "Invoice"
  },
  "split_document": {
    "page_indices": [0, 1]
  },
  "inference_result": {
    "Agency": "American Media & Advocacy Group",
    "Advertiser": "National Rifle Association",
    "GrossTotal": 15185.0,
    "PaymentTerms": "30 Days",
    "AgencyCommission": 2277.75,
    "NetAmountDue": 12907.25,
    "LineItems": [
      {
        "LineItemDescription": "M-F 1135p-1205a",
        "LineItemStartDate": "10/09/12",
        "LineItemEndDate": "10/09/12",
        "LineItemDays": "-T-----",
        "LineItemRate": 600.0
      }
    ]
  }
}

Noise Pipelines

18 distinct Augraphy-based degradation pipelines covering a wide range of real-world scan/print artifacts:

Augraphy Archetypes (pre-built pipelines)

Pipeline	Description
`default`	Balanced general-purpose degradation
`archetype3`	Heavy post-processing effects
`archetype4`	Minimal geometric distortions
`archetype7`	Color and lighting variations
`archetype9`	Texture-based degradations
`archetype10`	Scanner artifact simulation
`archetype11`	Complex multi-phase pipeline

Custom Pipelines (research-designed)

Pipeline	Key Augmentations	Simulates
`custom12`	DirtyDrum + DirtyRollers	Scanner roller artifacts
`custom13`	Stains + Folding	Physical document damage
`custom14`	BleedThrough + InkMottling	Ink bleed and mottling
`custom15`	Moire + ColorPaper	Scanning/aging effects
`custom16`	ShadowCast + LightingGradient	Uneven lighting
`custom17`	Jpeg + SubtleNoise	Compression artifacts
`custom18`	Geometric + PageBorder	Alignment issues
`custom19`	BindingsAndFasteners + Letterpress	Binding shadows
`custom20`	Brightness + BadPhotoCopy	Photocopy quality
`custom21`	WaterMark + NoisyLines	Overlaid artifacts
`custom22`	Dithering + DotMatrix	Dot-matrix printing

Entity Fields

Field	Type	Description
`Agency`	string	Advertising agency name
`Advertiser`	string	Client/advertiser name
`GrossTotal`	float	Total invoice amount (USD)
`PaymentTerms`	string	Payment terms (e.g., "30 Days")
`AgencyCommission`	float	Agency commission amount (USD)
`NetAmountDue`	float	Net amount after commission (USD)
`LineItems`	list	Individual line items
`LineItemDescription`	string	Program/slot description
`LineItemStartDate`	string	Airing start date (MM/DD/YY)
`LineItemEndDate`	string	Airing end date (MM/DD/YY)
`LineItemDays`	string	Days of week pattern (e.g., "-T-----")
`LineItemRate`	float	Cost per line item (USD)

Intended Use

This dataset is intended for:

Confidence calibration research — Measuring model confidence under varying degrees of document degradation.
OCR robustness evaluation — Benchmarking OCR and KIE systems on realistic noisy documents.
Document understanding — Evaluating models on structured information extraction from invoices (evaluation/benchmark use only; no training split is provided).
Noise impact analysis — Studying how specific noise types affect extraction accuracy per field.

Source Data

The 75 clean FCC invoices used in this dataset are taken from RealKIE-FCC-Verified, which re-annotated the FCC Invoices subset of the original RealKIE benchmark (Townsend et al., 2024) to fix two issues in the original annotations:

Line Item Grouping — fields previously treated as independent entries are grouped within each individual line item, aligning annotations with real-world invoice structure.
Annotation Corrections — erroneous values in the original annotations were corrected.

Starting from these 75 verified documents and their corrected ground truth (json_response), this dataset adds noise augmentation using the Augraphy library with OCR-safe parameter settings, producing up to 18 degraded variants per document for confidence calibration and OCR robustness research.

How to Cite This Dataset

If you use this dataset, please cite:

@misc{islam2026confidencecalibration,
  title     = {FCC Invoices Verified Augmented: A Benchmark for Confidence Calibration in Document Understanding under Noise},
  author    = {Md Mofijul Islam and Mohammad Rostami and others},
  year      = {2026},
  note      = {Dataset available at Hugging Face},
  url       = {https://huggingface.co/datasets/amazon/ConfBench}
}

License

This dataset is released under CC BY-NC 4.0. The original FCC invoice documents are public records.