humanize_numbers 1.0.1

Convert numbers into their word representation

Dart humanize number

Note

This package is partially based on number_to_string. However, since it doesn't add translations (Arabic and English) or is regularly maintained, I opted to create my own version that works with a wide variety of languages (and is more extensible and readable). In other words, this is a version that improves on absolutely every aspect of number_to_string.

A Dart library to convert numbers into their word representation in multiple languages. Perfect for financial, educational, or localization-focused applications.

Supported Languages

Language	Code	Example Input	Output (Word Form)
English (US)	en	2386	Two thousand three hundred eighty-six
German	de	2386	Zweitausenddreihundertsechsundachtzig
Japanese	ja	2386	二千三百八十六
Korean	ko	2386	이천삼백팔십육
Spanish	es	2386	Dos mil trescientos ochenta y seis
Portuguese	pt	2386	Dois mil trezentos e oitenta e seis
Arabic	ar	2386	ألفان وثلاثمائة وستة وثمانون

Usage Example

import 'package:humanize_numbers/humanize_numbers.dart';

void main() {
  final humanizer = HumanizeNumber();
  humanizer.settings(currency: 'USD');
  
  print(humanizer.parse(2386, 'en')); // Output: Two thousand three hundred eighty-six USD
  print(humanizer.lastResult); // ----------------^
  print(humanizer.lastNumberParsed); // 2386
  print(humanizer.currentTranslationLanguage); // en
}

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Language-Specific Examples

German (Deutsch)

// 2386 → "Zweitausenddreihundertsechsundachtzig"

humanizer.parse(2386, 'de');

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Arabic (العربية)

// 1000001 → `مليون وواحد`

humanizer.parse(1000001, 'ar');

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Spanish (Español)

// 1500 → "Mil quinientos"

humanizer.parse(1500, 'es');

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You can also customize

Settings

Configure prefixes, suffixes, or currency:

humanizer.settings(
  prefix: 'Total: ',
  suffix: ' (final amount)',
  currency: '€',
);
print(humanizer.parse(20, 'es'));   
// Output: "Total: Veintiseis € (final ammount)"

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Key Concepts

1. Number Grouping & Hierarchy

Numbers are decomposed into 3-digit groups (from right to left) to handle large values systematically.
This mirrors the natural language structure of numbers across most languages.

How It Works:

1. Reverse & Split:
   2,386 → reversed digits → ["6", "8", "3", "2"] → grouped as [["6", "8", "3"], ["2"]].
2. Process Groups:
   Each group is mapped to its word equivalent (units, thousands, millions, etc.).
3. Recombine:
   Apply language-specific connectors between groups.

Example (2,386):

Group Value	Unit	German Output
2	Thousands	Zweitausend
300	Hundreds	dreihundert
86	Tens/Units	sechsundachtzig

2. Connector Rules & Positioning

Connectors glue number parts together. Rules vary dramatically between languages:

Connector Types:

Language	Connector	Placement Rule	Example (86)
German	und	Between units and tens: units + und + tens	sechsundachtzig
English	-	Between tens and units: tens + - + units	eighty-six
Arabic	و	Between all non-zero groups	ستة وثمانون
Spanish	y	Between tens and units only	ochenta y seis

3. Special Cases Handling

A. Zero & Empty Groups

• Zero: Explicitly handled (e.g., Arabic صفر vs. English omitting it in 1000).
• Empty Groups: Skipped (e.g., 1,000,000 → eine Million, not eine Million null).

B. Teen Numbers (11-19)

Treated as unique words in most languages:

• English: eleven, twelve (not ten-one).
• German: elf, zwölf.

C. Pluralization (Spanish)

• Spanish: ciento (101) vs. cien (100).

Contributing

We greatly appreciate your time and effort.

To keep the project consistent and maintainable, we have a few guidelines that we ask all contributors to follow. These guidelines help ensure that everyone can understand and work with the code easier.

See Contributing for more details.