text_comparison_score_codespark 1.0.0

String similarity for Dart & Flutter. Calculate match percentages using Levenshtein, Damerau-Levenshtein, and Jaro-Winkler. Supports fuzzy matching and case-insensitive comparison.

text_comparison_score_codespark

Calculate string similarity, text comparison scores, match percentages, fuzzy matching results, and string distance metrics using Levenshtein Distance, Damerau-Levenshtein Distance, Jaro-Winkler, and other text comparison algorithms in Dart and Flutter.

Built by Katayath Sai Kiran · @Katayath-Sai-Kiran

Screenshots

Score overview

Algorithm comparison

Edge cases

Features

• Levenshtein Distance: Calculates the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one string into the other.
• Damerau-Levenshtein Distance: Extends Levenshtein distance by treating adjacent character transpositions (e.g. "teh" → "the") as a single edit, producing more accurate scores for real-world typos.
• Jaro-Winkler Distance: Measures the similarity between two strings, taking into account the number of matching characters and transpositions, with a boost for common prefixes.
• Match Percentage: Returns the match percentage between two strings, indicating how similar they are.
• Case Sensitivity Option: Allows optional case sensitivity in string comparisons.
• Multiple Algorithms: Choose between different algorithms — Levenshtein, Damerau-Levenshtein, and Jaro-Winkler — for your comparison needs.

Use Cases

• String similarity detection
• Text similarity analysis
• Fuzzy string matching
• Fuzzy search
• String comparison
• Text comparison
• Match percentage calculation
• Confidence score generation
• Typo detection
• Search suggestions
• Duplicate record matching
• Name matching
• Data validation

Installation

Add the following to your pubspec.yaml:

dependencies:
  text_comparison_score_codespark: ^1.0.0

Then run:

flutter pub get

Usage

Here's how to use the TextComparisonScore class to calculate the match percentage between two strings using different algorithms:

import 'package:text_comparison_score_codespark/text_comparison_score_codespark.dart';

void main() {
  // Example 1: Simple Levenshtein comparison
  String string1 = "kitten";
  String string2 = "sitting";

  double matchPercent = TextComparisonScore.calculateScore(string1, string2, algorithm: ComparisonAlgorithm.levenshtein);
  print("Levenshtein Match Percentage between '$string1' and '$string2': $matchPercent%");

  // Example 2: Jaro-Winkler comparison
  double jaroMatchPercent = TextComparisonScore.calculateScore(string1, string2, algorithm: ComparisonAlgorithm.jaroWinkler);
  print("Jaro-Winkler Match Percentage between '$string1' and '$string2': $jaroMatchPercent%");

  // Example 3: Identical strings
  String identical1 = "flutter";
  String identical2 = "flutter";

  double identicalMatchPercent = TextComparisonScore.calculateScore(identical1, identical2);
  print("Match Percentage between identical strings '$identical1' and '$identical2': $identicalMatchPercent%");

  // Example 4: Case insensitive comparison
  String caseSensitive1 = "Hello";
  String caseSensitive2 = "hello";

  double caseSensitiveMatchPercent = TextComparisonScore.calculateScore(caseSensitive1, caseSensitive2, caseSensitive: false);
  print("Match Percentage between '$caseSensitive1' and '$caseSensitive2' (case insensitive): $caseSensitiveMatchPercent%");

  // Example 5: Damerau-Levenshtein — better typo handling via transpositions
  // "teh" vs "the" is a single transposition; standard Levenshtein counts it as 2 edits
  String typo = "teh";
  String correct = "the";

  double dlMatchPercent = TextComparisonScore.calculateScore(typo, correct, algorithm: ComparisonAlgorithm.damerauLevenshtein);
  double levMatchPercent = TextComparisonScore.calculateScore(typo, correct, algorithm: ComparisonAlgorithm.levenshtein);
  print("Damerau-Levenshtein Match Percentage between '$typo' and '$correct': $dlMatchPercent%");
  print("Levenshtein Match Percentage between '$typo' and '$correct': $levMatchPercent%");
}

Example Output

- **Levenshtein Match Percentage between** `'kitten'` **and** `'sitting'`: `57.14285714285714%`
- **Jaro-Winkler Match Percentage between** `'kitten'` **and** `'sitting'`: `74.74%`
- **Match Percentage between identical strings** `'flutter'` **and** `'flutter'`: `100.0%`
- **Match Percentage between** `'Hello'` **and** `'hello'` **(case insensitive)**: `100.0%`
- **Damerau-Levenshtein Match Percentage between** `'teh'` **and** `'the'`: `66.67%`
- **Levenshtein Match Percentage between** `'teh'` **and** `'the'`: `33.33%`

Future Updates

In future versions, this package will include:

1. Cosine Similarity: Measures the cosine of the angle between two vectors, which can be used for similarity between text strings.
2. Soundex: A phonetic algorithm for indexing names by sound, as pronounced in English.
3. Hamming Distance: Measures the number of differing bits between two binary strings.
4. Normalized Distance Measures: Provides normalized versions of distance metrics to return values between 0 and 1.
5. String Tokenization & N-grams: Support for splitting strings into tokens and analyzing n-grams.
6. Customizable Weighting: Allows users to assign custom weights to different types of edits.
7. Multi-Language Support: Ensures that algorithms work with various character sets and languages.
8. Threshold-based Matching: Returns whether the match percentage is above a user-defined threshold.
9. Performance Optimization for Large Texts: Implements efficient data structures and parallel processing to handle large texts.
10. Batch Comparison: Allows users to compare a single string against a batch of other strings, returning the most similar ones.
11. Detailed Comparison Report: Provides a detailed report with multiple similarity metrics between two strings.
12. API for Custom Comparison Functions: Enables users to define and plug in their custom comparison functions.

Maintainer

Developed with 💙 by Katayath Sai Kiran Feel free to contribute or suggest improvements!