---
configs:
- config_name: default
  data_files:
  - split: train
    path: "metadata.csv"
task_categories:
- object-detection
tags:
- financial
- document-ai
- multimodal
pretty_name: FinDoc-Robust
size_categories:
- 10K<n<100K
license: apache-2.0
language:
- en
dataset_info:
  features:
  - name: file_name
    dtype: string
  - name: document_type
    dtype: string
  - name: document_id
    dtype: int64
  - name: clean_pdf
    dtype: string
  - name: clean_xlsx
    dtype: string
  - name: clean_bbox_px
    dtype: string
  - name: clean_bbox_pdf_pt
    dtype: string
  - name: dirty_1_image
    dtype: string
  - name: dirty_1_bbox
    dtype: string
  - name: dirty_2_image
    dtype: string
  - name: dirty_2_bbox
    dtype: string
  - name: dirty_3_image
    dtype: string
  - name: dirty_3_bbox
    dtype: string
  - name: dirty_4_image
    dtype: string
  - name: dirty_4_bbox
    dtype: string
  - name: dirty_5_image
    dtype: string
  - name: dirty_5_bbox
    dtype: string
---

# Financial Document Extraction & Robustness Dataset (FinDoc-Robust)

## Dataset Description
FinDoc-Robust is a multimodal, benchmark-grade dataset designed for **Document Layout Analysis (DLA)**, **Visual Information Extraction (VIE)**, and evaluating model robustness against real-world degradation. 

The dataset contains financial reports across **5 distinct document categories** (e.g., cash flow statements, balance sheets, trial balances, shareholders' equity, corporate income statements). For every document, it provides perfect digital vectors, tabular ground truths, pixel-level bounding boxes, and **5 structurally degraded ("dirty") variants** simulating camera captures, scans, and physical artifacts.

### Key Applications
* **Robust Document AI:** Training models to resist geometric distortions, noise, and blur.
* **Table Reconstruction:** Benchmarking end-to-end Image-to-Excel/HTML/Markdown pipelines.
* **Multimodal Alignment:** Fine-tuning models like LayoutLMv3, Donut, or proprietary Vision-LLMs on complex financial structures.

---

## Dataset Structure

The repository is organized hierarchically by document type and numerical index. Each sample folder contains a complete sub-set of modalities:

```text
dataset_root/
├── new_type_cash_flow_statement/
├── new_type_shareholders_equity/
├── new_type_trial_balance/
├── pro_doc_corporate_income_statement/
└── pro_doc_full_balance_sheet/
    ├── 001/
    │   ├── 001.pdf             # Original clean vector PDF
    │   ├── 001.png             # Rendered high-res image (clean)
    │   ├── 001.xlsx            # Target ground-truth table structure
    │   ├── 001.json            # Word/Phrase Bounding Boxes (Pixel space)
    │   ├── 001_pdf.json        # Word/Phrase Bounding Boxes (DTP Point space)
    │   ├── 001_dirty_1.png     # Degraded scan/photo simulation variant 1
    │   ├── 001_dirty_1.json    # Adjusted Bounding Boxes for variant 1
    │   ...
    │   ├── 001_dirty_5.png     # Degraded variant 5
    │   └── 001_dirty_5.json    # Adjusted Bounding Boxes for variant 5
    └── 1001/                   # Scale-tested deep indices (up to 4 digits)
        ...
```


---

## Modality Specifications

### 1. Ground Truth Structures

* **`.pdf`**: Original vector file preserving strict semantic layout.
* **`.xlsx`**: The ideal downstream target layout. Contains finalized cell alignments, structures, and text groups.

### 2. Multi-Coordinate Bounding Boxes (`.json`)

The dataset includes two coordinate topologies to match different ingestion pipelines:

* **`001_pdf.json` (Vector Scale):** Stored in DTP Points ($1 \text{ inch} = 72 \text{ points}$), native to engines like PyMuPDF or `pdfplumber`. Origin is typically evaluated from Top-Left or Bottom-Left depending on the parser.
* **`001.json` (Raster Scale):** Mapped directly to high-resolution pixel coordinates matching the native `001.png` dimensions (e.g., A4 at 200 DPI: $1654 \times 2339 \text{ px}$).

### 3. Robustness & Degradation Layers (`_dirty_X`)

Each baseline sheet is supplemented with 5 alternative states mimicking typical pipeline damage:

* Sensor noise, blur, and lighting gradients.
* Rotation, skewing, and affine perspective warps.
* Contrast loss and compression artifacts.

Every dirty image has a corresponding `.json` containing transformed bounding box parameters adjusted to the physical distortion.

---

## JSON Schema Example

```json
{
  "img_file": "001.png",
  "img_width": 1654,
  "img_height": 2339,
  "labels": [
    {
      "text": "CASH FLOWS FROM CORE OPERATIONS",
      "bbox_px": [173.1, 415.24, 781.99, 446.65]
    }
  ]
}

```

*Note: `bbox_px` format is `[xmin, ymin, xmax, ymax]`.*

---

## Usage & Evaluation

```python
from datasets import load_dataset

# Configuration for loading the structured layout hierarchy
# Dataset script coming soon
dataset = load_dataset("arcolab-dev/FinDoc-Robust")

```

### Recommended Evaluation Metrics.

* **Tree-edit distance / TEDS:** For structural table matching via the `.xlsx` layout.
* **ANLS (Average Normalized Levenshtein Similarity):** For text extraction robustness under dirty variants.
* **mAP (mean Average Precision):** For word/cell layout extraction bounding boxes.

```