README.md

metadata

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:

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

{
  "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

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.