Datasets:
File size: 5,334 Bytes
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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.
``` |