Addris

Addris is an end-to-end address intelligence platform that extracts addresses from delivery documents and optimizes multi-stop delivery routes. Couriers capture delivery images with the mobile app, and the FastAPI backend processes them using advanced OCR, LLM-based extraction, address parsing, geocoding, and route optimization.

Features

Multiple Extraction Strategies: Supports three extraction methods:
- VLM (Vision Language Model): Direct image-to-address extraction using multimodal LLMs
- OCR + LLM: OCR text extraction followed by LLM-based address parsing
- OCR Sliding Window: Traditional OCR with sliding window text candidate generation
Flexible LLM Integration: Supports multiple LLM providers (OpenAI, Anthropic, Google, XAI, Ollama/local)
Dual OCR Engines: EasyOCR (default) or Tesseract for text extraction
Address Validation: Libpostal-based parsing with confidence scoring and geocoding validation
Route Optimization: OR-Tools-based TSP solver with Google Maps integration for traffic-aware routing
Mobile-First: Expo React Native app with real-time status updates and navigation handoff

System Architecture

Workflow

Image Capture: Mobile app captures or imports delivery document images (JPEG/PNG)
Address Extraction (backend):
- Strategy Selection: Chooses extraction method based on ADDRIS_EXTRACTION_STRATEGY (see Extraction Strategies section)
- Validation: Parsed addresses are validated and geocoded to lat/lon coordinates
- Confidence Scoring: Combines OCR/LLM confidence with geocoder feedback
Route Optimization: Selected addresses are submitted to route optimization endpoint
- Distance matrix computation (Google Maps API or Haversine fallback)
- TSP solving with OR-Tools for optimal stop ordering
- Traffic-aware ETAs and toll cost estimation
Navigation: Mobile app opens optimized route in Google Maps with waypoints

Technology Stack

Backend:

FastAPI for async REST API
EasyOCR/Tesseract for OCR
LangChain for LLM orchestration
Libpostal for address parsing
OR-Tools for route optimization
Google Maps API / Nominatim for geocoding
Pydantic for data validation

Mobile:

Expo (React Native) for cross-platform mobile app
React Query for state management
Expo Image Picker for camera/gallery access
Expo Location for GPS and reverse geocoding
React Native Map Link for navigation handoff

Repository Structure

Addris/
├── backend/              # FastAPI backend service
│   ├── app/
│   │   ├── api/         # REST API routers (v1)
│   │   │   ├── routes.py
│   │   │   └── v1/
│   │   │       ├── addresses.py  # Address extraction endpoint
│   │   │       └── routes.py     # Route optimization endpoint
│   │   ├── core/        # Configuration, logging
│   │   ├── domain/      # Domain models (address, geocoding, geometry, optimization)
│   │   ├── schemas/     # Pydantic request/response models
│   │   └── services/   # Business logic (OCR, parsing, geocoding, routing, LLM, storage)
│   │       ├── address_service.py
│   │       ├── ocr.py
│   │       ├── llm.py
│   │       ├── parsing.py
│   │       ├── geocoding.py
│   │       ├── routing.py
│   │       └── storage.py
│   ├── tests/           # Test suite
│   └── pyproject.toml   # Python dependencies
├── mobile/              # Expo React Native mobile app
│   ├── src/
│   │   ├── api/
│   │   │   └── addris.js  # API client
│   │   └── components/   # UI components
│   └── package.json
├── assets/              # Screenshots and images
├── OVERVIEW.md          # Project requirements and goals
└── README.md            # This file

Prerequisites

Backend Requirements

Component	Requirement	Installation	Notes
Python	3.10+	Managed via `uv`	`uv` automatically downloads Python if missing
uv	Latest	`curl -LsSf https://astral.sh/uv/install.sh \| sh`	Fast Python package manager
Libpostal	Native library	macOS: `brew install libpostal`	Required for address parsing
Tesseract	Optional	macOS: `brew install tesseract`	Alternative OCR backend
libomp	Optional	macOS: `brew install libomp`	Improves OpenCV performance

Mobile Requirements

Component	Requirement	Installation	Notes
Node.js	18+	Use nvm	`nvm install 18`
npm	9+	Comes with Node.js	Or use `npm install -g npm@latest`
Expo CLI	Optional	`npm install -g expo`	Not required for `npx expo start`

Optional Services

Google Maps API Key: Enables Google geocoding, traffic-aware routing, and toll metadata
LLM API Keys: Required for VLM/OCR+LLM strategies:
- OpenAI: ADDRIS_OPENAI_API_KEY
- Anthropic: ADDRIS_ANTHROPIC_API_KEY
- Google: ADDRIS_GOOGLE_API_KEY
- XAI: ADDRIS_XAI_API_KEY
- Local (Ollama): No API key needed, set ADDRIS_LLM_PROVIDER=local

Quick Start

Backend Setup

# Navigate to backend directory
cd backend

# Install dependencies
# On macOS with Homebrew, set compiler flags for libpostal
CFLAGS="-I/opt/homebrew/include" LDFLAGS="-L/opt/homebrew/lib" \
    uv sync

# Create .env file (copy from .env.example)


# Start the FastAPI server
uv run uvicorn app.main:app --reload

The API will be available at http://127.0.0.1:8000:

API docs: http://127.0.0.1:8000/docs (Swagger UI)
ReDoc: http://127.0.0.1:8000/redoc
Health check: http://127.0.0.1:8000/health

Mobile App Setup

# Navigate to mobile directory
cd mobile

# Install dependencies
npm install

# Set backend API URL (adjust if backend runs on different host/port)
export EXPO_PUBLIC_API_URL="http://127.0.0.1:8000"

# Start Expo development server
npx expo start

Launch the app in:

Expo Go: Scan QR code with Expo Go app on your phone
iOS Simulator: Press i in the terminal (requires Xcode)
Android Emulator: Press a in the terminal (requires Android Studio)

Configuration

Backend Environment Variables

Create a .env file in the backend/ directory with the following variables:

Core Settings

Variable	Description	Default	Required
`ADDRIS_DEBUG`	Enable verbose logging and request traces	`false`	No
`ADDRIS_STORAGE_ROOT`	Directory for uploaded images and artifacts	`./data`	No

Extraction Strategy

Variable	Description	Options	Default
`ADDRIS_EXTRACTION_STRATEGY`	Address extraction method	`vlm`, `ocr_llm`, `ocr_sliding_window`	`vlm`
`ADDRIS_OCR_BACKEND`	OCR engine for text extraction	`easyocr`, `tesseract`	`easyocr`

LLM Configuration

Variable	Description	Default	Required for VLM/OCR+LLM
`ADDRIS_LLM_PROVIDER`	LLM provider	`openai`	Yes
`ADDRIS_LLM_MODEL`	Model name/identifier	`gemma3:4b`	No
`ADDRIS_LLM_BASE_URL`	Custom base URL (for local/Ollama)	`None`	No
`ADDRIS_OPENAI_API_KEY`	OpenAI API key	`None`	If provider=openai
`ADDRIS_ANTHROPIC_API_KEY`	Anthropic API key	`None`	If provider=anthropic
`ADDRIS_GOOGLE_API_KEY`	Google API key	`None`	If provider=google
`ADDRIS_XAI_API_KEY`	XAI API key	`None`	If provider=xai

Geocoding

Variable	Description	Default	Required
`ADDRIS_GEOCODER_PROVIDER`	Geocoding service	`nominatim`	No
`ADDRIS_GEOCODER_USER_AGENT`	User agent for Nominatim	`addris-geocoder`	Yes (Nominatim)
`ADDRIS_GEOCODER_DOMAIN`	Custom geocoder domain	`None`	No
`ADDRIS_GEOCODER_TIMEOUT`	Request timeout (seconds)	`10.0`	No
`ADDRIS_GOOGLE_MAPS_API_KEY`	Google Maps API key	`None`	If provider=google

Routing

Variable	Description	Default	Required
`ADDRIS_ROUTING_DISTANCE_PROVIDER`	Distance matrix provider	`google`	No
`ADDRIS_ROUTING_USE_TRAFFIC`	Enable traffic-aware ETAs	`true`	No
`ADDRIS_ROUTING_DISTANCE_TIMEOUT`	Request timeout (seconds)	`10.0`	No
`ADDRIS_GOOGLE_MAPS_API_KEY`	Google Maps API key	`None`	If provider=google

Mobile Environment Variables

Set EXPO_PUBLIC_API_URL to point to your backend:

# In shell
export EXPO_PUBLIC_API_URL="http://127.0.0.1:8000"

# Or in .env file (requires expo-constants or similar)
EXPO_PUBLIC_API_URL=http://127.0.0.1:8000

See Expo environment variables documentation for more details.

Testing

Backend Tests

cd backend

# Install dev dependencies (pytest, ruff, mypy)
uv sync --extra dev

# Run all tests
uv run pytest

# Run specific test file
uv run pytest tests/test_address_parser.py

The test suite covers:

Address parsing and validation
Geocoding helpers
Route optimization
Address extraction service
API endpoints

API Documentation

Endpoints

`POST /v1/addresses/extract`

Extract addresses from an uploaded image.

Request:

Content-Type: multipart/form-data
Body: image (file, JPEG or PNG)

Response:

{
  "addresses": [
    {
      "raw_text": "123 Main St, Boston, MA 02101",
      "parsed": {
        "house_number": "123",
        "road": "Main St",
        "city": "Boston",
        "state": "MA",
        "postcode": "02101"
      },
      "latitude": 42.3601,
      "longitude": -71.0589,
      "confidence": 0.95,
      "status": "validated",
      "geocode_message": "Successfully geocoded"
    }
  ]
}

The extraction strategy is configured via ADDRIS_EXTRACTION_STRATEGY. See the Extraction Strategies section for details.

`POST /v1/routes/`

Compute optimized delivery route.

Request:

{
  "origin": {
    "latitude": 42.3601,
    "longitude": -71.0589,
    "label": "Current Location"
  },
  "stops": [
    {
      "latitude": 42.3656,
      "longitude": -71.0096,
      "label": "123 Main St"
    }
  ]
}

Response:

{
  "route": [
    {
      "order": 0,
      "label": "Current Location",
      "latitude": 42.3601,
      "longitude": -71.0589,
      "distance_meters": 0,
      "eta_seconds": 0
    },
    {
      "order": 1,
      "label": "123 Main St",
      "latitude": 42.3656,
      "longitude": -71.0096,
      "distance_meters": 4500,
      "eta_seconds": 600,
      "has_toll": false
    }
  ],
  "total_distance_meters": 4500,
  "total_eta_seconds": 600,
  "distance_provider": "google",
  "uses_live_traffic": true
}

`GET /health`

Health check endpoint for monitoring.

Response:

{
  "status": "ok"
}

API Documentation

Once the server is running:

Swagger UI: http://127.0.0.1:8000/docs
ReDoc: http://127.0.0.1:8000/redoc

Extraction Strategies

Addris supports three extraction strategies, each with different strengths:

1. VLM (Vision Language Model) - Default

Best for: High-quality images, complex layouts, handwritten addresses

Directly processes images with multimodal LLMs
No OCR step required
Best accuracy for complex documents
Requires LLM API key

Configuration:

ADDRIS_EXTRACTION_STRATEGY=vlm
ADDRIS_LLM_PROVIDER=openai  # or anthropic, google, xai, local

2. OCR + LLM

Best for: Text-heavy documents, multiple addresses per image

OCR extracts all text from image
LLM parses addresses from extracted text
Good balance of accuracy and cost
Requires LLM API key

Configuration:

ADDRIS_EXTRACTION_STRATEGY=ocr_llm
ADDRIS_OCR_BACKEND=easyocr  # or tesseract
ADDRIS_LLM_PROVIDER=openai

3. OCR Sliding Window

Best for: Simple documents, offline operation, no LLM dependency

OCR extracts text snippets with confidence scores
Sliding window generates address candidates
Libpostal parses and validates addresses
No LLM required (works offline)

Configuration:

ADDRIS_EXTRACTION_STRATEGY=ocr_sliding_window
ADDRIS_OCR_BACKEND=easyocr  # or tesseract

Mobile App Features

The Expo React Native app provides:

Image Capture: Camera and photo library access
Real-time Status: Live updates on extraction and routing progress
Address Selection: Toggle addresses on/off for route optimization
Location Services: GPS-based current location with reverse geocoding
Route Visualization: View optimized route with distance, ETA, and toll information
Navigation Handoff: Open route in Google Maps with waypoints

Development

See the Repository Structure section for the project layout.

Adding New Features

Backend: Add new endpoints in api/v1/, implement services in services/
Mobile: Add components in src/components/, update API client in src/api/
Tests: Add tests in backend/tests/ following existing patterns

Logging

Structured logging is enabled by default. Set ADDRIS_DEBUG=true for verbose request traces and detailed error information.

License

MIT

Contributing

This is an academic project. For questions or issues, please create an issue.

Name		Name	Last commit message	Last commit date
Latest commit History 24 Commits
assets		assets
backend		backend
mobile		mobile
.gitignore		.gitignore
LICENSE		LICENSE
OVERVIEW.md		OVERVIEW.md
README.md		README.md

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Addris

Features

System Architecture

Workflow

Technology Stack

Repository Structure

Prerequisites

Backend Requirements

Mobile Requirements

Optional Services

Quick Start

Backend Setup

Mobile App Setup

Configuration

Backend Environment Variables

Core Settings

Extraction Strategy

LLM Configuration

Geocoding

Routing

Mobile Environment Variables

Testing

Backend Tests

API Documentation

Endpoints

POST /v1/addresses/extract

POST /v1/routes/

GET /health

API Documentation

Extraction Strategies

1. VLM (Vision Language Model) - Default

2. OCR + LLM

3. OCR Sliding Window

Mobile App Features

Development

Adding New Features

Logging

License

Contributing

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`POST /v1/routes/`

`GET /health`

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