mailfix 0.37.1

Package for email validation with domain correction suggestions using configurable similarity algorithms.

Mailfix

A powerful Dart/Flutter package for email validation and correction suggestions using advanced string similarity algorithms.

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Features

• 📧 RFC 5322 compliant email validation (optional)
• 🔍 Smart domain suggestions for typos
• 🌐 Multiple similarity algorithms:
  • Levenshtein distance
  • Damerau-Levenshtein distance
  • Jaro-Winkler similarity
• 🎯 Customizable domain validation
• ⚙️ Configurable similarity threshold

Installation

dependencies:
  mailfix: ^0.0.5

Usage

Simple Example

import 'package:mailfix/mailfix.dart';

void main() {
  final mailfix = Mailfix();
  final result = mailfix.validateEmail('[e-mail]');
  print(result.suggestion); // Suggests: [e-mail]
}

Basic Usage

import 'package:mailfix/mailfix.dart';

void main() {
  final mailfix = Mailfix();
  
  final result = mailfix.validateEmail('[e-mail]');
  if (!result.isValid) {
    if (result.suggestion != null) {
      print('Suggestion: \\${result.suggestion}'); // Will suggest gmail.com
    } else {
      print('Error: \\${result.error}');
    }
  }
}

Custom Configuration

final mailfix = Mailfix(
  // Choose similarity algorithm
  algorithm: MailfixSimilarityAlgorithm.damerauLevenshtein,
  // Configure similarity threshold
  maxAllowedDistance: 3,
);

// Add custom domains
mailfix.addDomain('company.com');
mailfix.addDomains(['domain1.com', 'domain2.com']);

Custom Validator

class MyEmailValidator implements EmailValidatorInterface {
  @override
  bool isValid(String email) {
    // Your custom validation logic
    return true;
  }
}

Live Example

Test the Mailfix library right now:

1. Visit our interactive demo
2. Type an email with typos (e.g., "[e-mail]")
3. See the correction suggestions in real time

The complete example code is available in the repository for you to implement in your project.

Algorithms

Jaro-Winkler Similarity (Default)

Optimized for short strings and gives more favorable ratings to strings that match from the beginning. Good for catching subtle differences in domain names. Read more

Levenshtein Distance

Best for general purpose use. Calculates the minimum number of single-character edits required to change one string into another. Read more

Damerau-Levenshtein Distance

Better for catching transposition errors (when two adjacent characters are swapped). Particularly useful for email domains where typos often involve character swaps. Read more

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.