---
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: doc_id
      dtype: string
    - name: pipeline_name
      dtype: string
    - name: original_pdf
      dtype: string
    - name: noisy_pdf
      dtype: string
    - name: ground_truth
      dtype:
        struct:
          - name: document_class
            dtype:
              struct:
                - name: type
                  dtype: string
          - name: inference_result
            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

```python
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:

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

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

```python
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`)

```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`)

```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

```python
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

```python
# 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**](https://huggingface.co/datasets/amazon-agi/RealKIE-FCC-Verified), which re-annotated the FCC Invoices subset of the original [**RealKIE**](https://indicodatasolutions.github.io/RealKIE/) benchmark ([Townsend et al., 2024](https://arxiv.org/abs/2403.20101)) 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](https://github.com/sparkfish/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:

```bibtex
@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](https://creativecommons.org/licenses/by-nc/4.0/). The original FCC invoice documents are public records.