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```markdown
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# Financial Document Extraction & Robustness Dataset (FinDoc-Robust)
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## Dataset Description
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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.
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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.
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### Key Applications
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* **Robust Document AI:** Training models to resist geometric distortions, noise, and blur.
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* **Table Reconstruction:** Benchmarking end-to-end Image-to-Excel/HTML/Markdown pipelines.
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* **Multimodal Alignment:** Fine-tuning models like LayoutLMv3, Donut, or proprietary Vision-LLMs on complex financial structures.
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## Dataset Structure
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The repository is organized hierarchically by document type and numerical index. Each sample folder contains a complete sub-set of modalities:
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```text
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dataset_root/
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├── new_type_cash_flow_statement/
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├── new_type_shareholders_equity/
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├── new_type_trial_balance/
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├── pro_doc_corporate_income_statement/
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└── pro_doc_full_balance_sheet/
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├── 001/
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│ ├── 001.pdf # Original clean vector PDF
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│ ├── 001.png # Rendered high-res image (clean)
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│ ├── 001.xlsx # Target ground-truth table structure
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│ ├── 001.json # Word/Phrase Bounding Boxes (Pixel space)
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│ ├── 001_pdf.json # Word/Phrase Bounding Boxes (DTP Point space)
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│ ├── 001_dirty_1.png # Degraded scan/photo simulation variant 1
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│ ├── 001_dirty_1.json # Adjusted Bounding Boxes for variant 1
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│ ...
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│ ├── 001_dirty_5.png # Degraded variant 5
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│ └── 001_dirty_5.json # Adjusted Bounding Boxes for variant 5
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└── 1001/ # Scale-tested deep indices (up to 4 digits)
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```
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