# Handling Poorly Formatted Ghana Addresses

This document outlines the process for handling poorly formatted or unstructured Ghana addresses using the PyAddress library's GitHub integration.

## The Challenge of Poorly Formatted Addresses

Address data often comes in inconsistent formats with various issues:
- Components in the wrong order
- Missing line breaks or incorrect punctuation
- Mixed capitalization
- Missing components
- Redundant information

For Ghana addresses specifically, common formatting issues include:
- City and country placed at the beginning instead of the end
- Missing postal codes
- House numbers merged with street names
- Inconsistent handling of suburb/district information

## Bidirectional Process for Handling Poor Formats

PyAddress uses a two-stage process to handle poorly formatted Ghana addresses:

1. **Extract & Format**: Extract components from unstructured text and format using templates
2. **Parse & Validate**: Parse the formatted address back into structured components

This bidirectional approach ensures we can both standardize address display and create structured data.

## Demo Results

Our demonstration processed these poorly formatted Ghana addresses:

### Case 1: All on one line with incorrect order
```
ACCRA GHANA, 21 independence avenue, ridge, GA-059-0782, Ghana Technology Hub
```

After processing:
```
21 Independence Avenue
Accra, GA-059-0782
Ghana
```

### Case 2: Missing components, wrong order
```
University of Ghana, Accra Ghana Legon Road
```

After processing:
```
Legon Road
Accra
Ghana
```

### Case 3: Incorrect punctuation and capitalization
```
kumasi,ghana
45,bantama high street
Kumasi Market Office
```

After processing:
```
45 Bantama High Street
Kumasi
Ghana
```

### Case 4: Redundant information and mixed formats
```
GHANA (GH)
Accra City
Ridge Area
21 Independence Ave.
Postal: GA-059-0782
Ghana Tech Hub
```

After processing:
```
21 Independence Avenue
Ridge
Accra, GA-059-0782
Ghana
```

## Detailed Processing Flow

### 1. Component Extraction from Unstructured Text

The first step extracts potential address components from poorly formatted text:

- **Country Identification**: Identify "Ghana" to set country and country_code
- **Postal Code Extraction**: Use regex to find Ghana's standard postal code format (XX-XXX-XXXX)
- **City Recognition**: Match against known Ghana cities (Accra, Kumasi, etc.)
- **Road Name Extraction**: Use patterns to identify road names with common suffixes (Road, Street, Avenue)
- **House Number Identification**: Look for numeric prefixes to road names
- **Organization Name Detection**: Use keyword patterns to extract business/organization names
- **Suburb/District Recognition**: Identify known district names (Ridge, Legon, etc.)

For example, from `ACCRA GHANA, 21 independence avenue, ridge, GA-059-0782, Ghana Technology Hub`, we extract:
```python
{
  "country": "Ghana",
  "country_code": "GH",
  "postcode": "GA-059-0782",
  "city": "Accra",
  "house_number": "21",
  "road": "Independence Avenue",
  "suburb": "Ridge",
  "name": "Ghana Technology Hub"
}
```

### 2. Initial Formatting Using GitHub Templates

Once components are extracted, we attempt to format them using the GitHub-based templates:

- The country code "GH" selects the appropriate Ghana template
- The template determines the component order and format
- Missing components are handled according to the template rules

This process leverages the GitHub integration by using templates from the OpenCageData repository to ensure proper formatting according to Ghana standards.

### 3. Structured Component Extraction from Formatted Address

After formatting, we extract structured components from the standardized address:

- Split the formatted address into lines
- Identify each line's purpose based on position and content
- Extract individual components using pattern recognition
- Set proper data types and normalize values

### 4. Final Validation through Reformatting

As a final validation step, we reformat the extracted components:

- Use the components to generate a final formatted address
- Verify that the format matches Ghana's addressing conventions
- Ensure all required components are present and properly ordered

## GitHub Integration's Role in Poor Format Handling

The GitHub integration is crucial for handling poorly formatted addresses:

1. **Template Selection**: Provides country-specific templates for proper formatting
2. **Component Order**: Defines the correct order of address components for Ghana
3. **Validation Rules**: Supplies rules for validating address components
4. **Format Standardization**: Ensures consistent output formatting for Ghana addresses

## Implementation Challenges and Solutions

### Challenge: Inconsistent Input Formats

**Solution**: 
- Use flexible regex patterns that can handle variations
- Normalize text by removing excess punctuation and standardizing spacing
- Implement multiple pattern matching approaches for each component

### Challenge: Missing Critical Components

**Solution**:
- Use default values when appropriate
- Implement fallback logic when critical components are missing
- Handle gracefully with clear error messages

### Challenge: Ambiguous Line Purpose

**Solution**:
- Use position-based heuristics (e.g., last line is country)
- Implement context-aware parsing based on Ghana address patterns
- Cross-validate components against expected formats

## Conclusion

Handling poorly formatted Ghana addresses demonstrates the flexibility and power of the PyAddress system and its GitHub integration. By using a bidirectional process of extraction, formatting, parsing, and validation, the system can transform messy, unstructured addresses into both standardized displays and structured data components.

This approach ensures that regardless of the input quality, addresses can be standardized according to Ghana's addressing conventions as defined in the GitHub templates, providing consistent and accurate address formatting. 