amazon/ConfBench

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

from huggingface_hub import snapshot_download

# Download the full dataset locally
local_dir = snapshot_download(repo_id="amazon/ConfBench", repo_type="dataset")

Or clone with git:

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

Or use the provided loader (see load_dataset.py in this release):

from load_dataset import FCCInvoicesDataset

ds = FCCInvoicesDataset(local_dir="/path/to/ConfBench")

for sample in ds:
    print(sample["doc_id"], sample["pipeline_name"])
    print(sample["ground_truth"]["inference_result"]["Agency"])

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/
└── assets/
    └── {doc_id}/
        ├── original.pdf              # Original clean PDF
        ├── gt.json                   # Ground truth annotations
        ├── metadata.json             # Pipeline manifest
        ├── default/
        │   └── default_noisy.pdf
        ├── archetype3/
        │   └── archetype3_noisy.pdf
        └── ... (16 more pipelines)

Ground Truth Schema (gt.json)

{
  "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
      }
    ]
  }
}

Metadata Schema (metadata.json)

{
  "doc_hash": "033f718b16cb597c065930410752c294",
  "original_pdf": "original.pdf",
  "ground_truth": "gt.json",
  "pipelines": [
    {"pipeline_name": "default", "noisy_pdf": "default/default_noisy.pdf"},
    {"pipeline_name": "archetype3", "noisy_pdf": "archetype3/archetype3_noisy.pdf"}
  ]
}

---

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)

---

Usage

Loading with huggingface_hub

from huggingface_hub import snapshot_download
import json
from pathlib import Path

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

doc_ids = [p.name for p in assets_dir.iterdir() if p.is_dir()]

# Load ground truth for a single document
def load_gt(doc_id):
    return json.loads((assets_dir / doc_id / "gt.json").read_text())

# List noisy PDFs for a document
def list_pipelines(doc_id):
    meta = json.loads((assets_dir / doc_id / "metadata.json").read_text())
    return meta["pipelines"]

Using the Provided Loading Script

# See load_dataset.py in this release
from load_dataset import FCCInvoicesDataset

ds = FCCInvoicesDataset(local_dir="/path/to/ConfBench")

# Iterate over all (doc, pipeline) pairs
for sample in ds:
    print(sample["doc_id"], sample["pipeline_name"])
    print(sample["ground_truth"]["inference_result"]["Agency"])

---

Intended Use

This dataset is intended for:

1. Confidence calibration research — Measuring model confidence under varying degrees of document degradation.
2. OCR robustness evaluation — Benchmarking OCR and KIE systems on realistic noisy documents.
3. Document understanding — Evaluating models on structured information extraction from invoices (evaluation/benchmark use only; no training split is provided).
4. 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:

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

Starting from these 75 verified documents and their corrected gt.json ground truth, 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.

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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.