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README.md

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+# 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.
+
 ---
-license: apache-2.0
+
+## 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)
+        ...
+```
+
+```markdown
+# 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.
+
+```
+
+```