Fuzzy Name Match

A lightweight and flexible fuzzy name matching library for Node.js, designed to handle variations, abbreviations, and reversed orders in personal names.

Features

Name-Specific Matching: Handles reversed names, abbreviations, and spacing variations
Configurable Thresholds: Control sensitivity of matches
Abbreviation Support: Detects and matches initials against full names
Flexible Comparison: Compare single pairs or batches of names
Lightweight: No heavy dependencies, works fast with Node.js projects

Installation

npm install fuzzy-name-match

Quick Start

const { nameMatchResult } = require('fuzzy-name-match');

// Compare two names and get detailed result
const result = nameMatchResult('Mr. Adam George Clooney', 'Adam G Clooney');

console.log(result);
/*
{
  result: true,
  score: 84.85,
  name_1: 'Mr. Adam George Clooney',
  name_2: 'Adam G Clooney',
  normalised_name_1: 'adam george clooney',
  normalised_name_2: 'adam g clooney'
}
*/

Detailed Usage

With Initials

import { nameMatchResult } from "fuzzy-name-match";

const result = nameMatchResult("S. Tendulkar", "Sachin Tendulkar");

console.log(result.result); // true
console.log(result.score);  // ~85

No Match

const result = nameMatchResult("Virat Kohli", "Rohit Sharma");
console.log(result.result); // false

Configuration

You can fine-tune the matching behavior using an optional config object:

interface INameMatchConfig {
  firstNameWeightage?: number;     // Weight given to first name (default: 70)
  reverseScoreThreshold?: number;  // Threshold for reverse name order (default: 90)
  nameMatchThreshold?: number;     // Final match threshold (default: 80)
}

Example with Config

const result = nameMatchResult("Ramesh Kumar", "Kumar Ramesh", {
  nameMatchThreshold: 85,
  reverseScoreThreshold: 85,
});

console.log(result);
/*
{
  result: true,
  score: 90,
  ...
}
*/

Return Format

Each call to nameMatchResult returns:

interface INameMatchResult {
  result: boolean;          // true if names match above threshold
  score: number;            // similarity score (0–100)
  name_1: string;           // original input name 1
  name_2: string;           // original input name 2
  normalised_name_1: string; // lowercased + cleaned name1
  normalised_name_2: string; // lowercased + cleaned name2
}

Use Cases

Data cleaning: Matching customer names across multiple databases.
Banking/Finance: Identifying duplicate KYC records.
Education: Matching student records where names are spelled differently.
E-commerce: Deduplicating customer accounts.

Challenges Handled
Reversed names (first last vs last, first)
Abbreviations and initials
Middle names and missing parts
Spacing and case variations
Common nickname handling (configurable)

License

MIT

Package detail

fuzzy-name-match

readme