README.md

---
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):

```python
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):

```python
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:

```bash
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`)

```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
      }
    ]
  }
}
```

---

## 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:

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 ground truth (`json_response`), 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.