README.md

metadata

title: Document Layout Detection
emoji: 📄
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.49.0
app_file: app.py
pinned: false
license: mit

📄 Document Layout, Table Structure & Signature Detection

A powerful AI-powered tool for automatically detecting document layout and structure, with an optional specialized handwritten signature detector.

🎯 What Does This Do?

This Space automatically analyzes your documents (PDFs, images, scanned documents) to:

• 🏷️ Detect Layout Elements: Identifies titles, headers, paragraphs, lists, tables, figures, captions, formulas, and more
• 📊 Extract Tables: Recognizes table structures and extracts data
• 🖼️ Visual Output: Shows bounding boxes around detected elements with color-coded labels
• 📝 Export Formats: Provides Markdown, JSON, and visual outputs
• 🔍 OCR Support: Automatically processes scanned documents and images
• ✍️ Signature Detection: Uses a fine-tuned YOLOv8s model to find handwritten signatures (overlay on layout view or run as a dedicated tool)

🚀 How to Use

1. Upload your document (PDF, JPG, PNG, etc.)
2. Choose processing mode:
   • Fast: Quick processing for simple documents
   • Accurate: Better quality for complex tables (slower)
3. Configure options:
   • Enable/disable OCR
   • Enable/disable table detection
4. Process and view results!

📚 Use Cases

Perfect for analyzing:

• 🆔 ID Documents: Aadhaar cards, passports, driver's licenses
• 📄 Forms & Applications: Government forms, surveys, questionnaires
• 🧾 Invoices & Receipts: Business documents with tables
• 📖 Research Papers: Academic documents with complex layouts
• 📊 Reports: Annual reports, financial statements
• 📰 Articles & Documents: Any structured document

🛠️ Technology

This Space uses state-of-the-art AI models:

• Layout Model: Advanced neural networks for document layout analysis
• Table Structure Model: TableFormer architecture for table detection and extraction
• OCR Engine: Integrated OCR for text recognition in scanned documents
• Framework: Modern document processing pipeline
• Signature Model (Optional): Finetuned signature detector (tech4humans/yolov8s-signature-detector) from Hugging Face

🎨 Output Formats

1. Visual Visualization

• Bounding boxes drawn on the document
• Color-coded by element type
• Labels showing detected elements

2. Markdown Export

• Clean, structured text output
• Preserves document hierarchy
• Ready for further processing

3. JSON Data

• Complete layout predictions
• Bounding box coordinates
• Element types and confidence scores
• Machine-readable format

🌟 Features

This tool offers:

• Advanced AI models for layout detection
• Supports multiple input formats (PDF, images)
• Accurate table structure extraction
• Handles both digital and scanned documents
• Exports to various formats (Markdown, JSON)
• Fast and accurate processing modes

🚀 Deployment on Hugging Face Spaces

This app is ready to deploy on Hugging Face Spaces!

Setup HF_TOKEN Secret

The signature detector model is gated and requires authentication:

1. Go to your Space settings: Settings → Repository secrets
2. Add a new secret:
   • Name: HF_TOKEN
   • Value: Your Hugging Face token (get it from https://huggingface.co/settings/tokens)
3. Click Add Secret

The app will automatically use this token to download the signature model on startup.

Requirements

• SDK: Gradio 5.x
• Python: 3.11+
• Hardware: CPU (2 cores, 18GB RAM on Spaces)
• Runtime: ~2-3 minutes first load (model downloads), then ~1-3s per inference

All dependencies are in requirements.txt and will be installed automatically.

🧪 Local Testing

Want to test locally?

# Install dependencies
pip install -r requirements.txt

# Set HF token (if signature model is gated)
export HF_TOKEN=hf_xxx

# Run the app locally
python app.py

Test Scripts

# Test signature detection only
python test_signature.py

# Test full document analysis
python test_analyze.py

Signature Detector Notes

• The signature model weights are hosted on Hugging Face (tech4humans/yolov8s-signature-detector)
• CPU inference is supported; no GPU required
• The app queues up to 2 concurrent jobs to align with Spaces CPU (2 cores)
• First run downloads ~12MB model checkpoint

📸 Examples

Signature-only examples live under sample_signature/. Try them in the "Signature Detection (Only)" tab.

OCR Engine

• This app uses RapidOCR with the ONNX Runtime backend by default when OCR is enabled, for fast and accurate CPU inference.
• If ONNXRuntime is missing, Docling may fall back to other engines; this repo includes onnxruntime in requirements.txt and configures RapidOcrOptions(backend="onnxruntime") to enforce the preferred engine.

🤝 Contributing

Found a bug or have a suggestion? Feel free to open an issue or contribute!

📝 License

• App code: MIT License
• Signature weights: AGPL-3.0 (see the model card on Hugging Face). Using the model in a network service may require making corresponding source available per AGPL.

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