🔢 Words to Number Converter
Convert English number words to digits with comprehensive parsing of complex numbers, support for millions, billions, and advanced number formats for text processing and data conversion.
📝 Convert Words to Numbers
Enter number words to convert to digits:
🔢 Understanding Number Words
English number words follow specific patterns and rules that make them easy to parse and convert to digits. Our converter handles complex numbers with multiple scales and compound forms.
📊 Number Word Structure
🔹 Units (0-19)
zero, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen
🔹 Tens (20-90)
twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety
🔹 Scales
hundred (×100), thousand (×1,000), million (×1,000,000), billion (×1,000,000,000), trillion (×1,000,000,000,000)
🔧 Parsing Rules
📝 Compound Numbers
twenty-one, thirty-five, forty-two
Hyphenated forms are treated as single units
Parsed as twenty + one = 21
📈 Scale Multiplication
two hundred = 2 × 100 = 200
three thousand = 3 × 1,000 = 3,000
Scale words multiply the preceding number
🔄 Accumulation
Numbers accumulate until a scale word
two thousand five hundred = 2,000 + 500 = 2,500
Each scale resets the accumulation
📚 Examples of Complex Numbers
| Words | Number | Parsing Breakdown |
|---|---|---|
| twenty-one | 21 | twenty + one |
| one hundred | 100 | 1 × 100 |
| two hundred fifty | 250 | (2 × 100) + 50 |
| one thousand | 1,000 | 1 × 1,000 |
| five thousand two hundred | 5,200 | (5 × 1,000) + 200 |
| one million | 1,000,000 | 1 × 1,000,000 |
| two billion five hundred million | 2,500,000,000 | (2 × 1,000,000,000) + (500 × 1,000,000) |
🌍 International Number Formats
🇺🇸 American English
1,000 (thousand)
1,000,000 (million)
1,000,000,000 (billion)
Uses commas as separators
🇬🇧 British English
1,000 (thousand)
1,000,000 (million)
1,000,000,000 (thousand million)
Different billion usage
🇪🇺 European Formats
1.000 (thousand)
1.000.000 (million)
Uses periods as separators
Differs by country
💼 Professional Applications
📄 Document Processing
Convert written numbers in contracts
Parse legal documents
Extract amounts from text
Automated data entry
🤖 Natural Language Processing
Text analysis tools
Voice-to-text conversion
Chatbot number recognition
AI language models
📊 Data Analysis
Survey response processing
Financial report parsing
Research data extraction
Automated reporting
🎓 Educational Tools
Language learning apps
Mathematics education
Spelling practice
Number recognition
🔧 Technical Implementation
⚙️ Algorithm Steps
1. Normalize input text
2. Tokenize into words
3. Map words to numeric values
4. Apply scale multipliers
5. Accumulate results
6. Handle special cases
🛠️ Edge Cases
Hyphenated numbers
Irregular forms
Scale word positioning
Conjunction handling
Error detection
Input validation
📈 Performance
O(n) time complexity
Linear word processing
Efficient dictionary lookup
Minimal memory usage
Fast execution
💡 Conversion Tip: Our converter handles complex number phrases like "two thousand five hundred and twenty-three" by processing scale words (thousand, million) as multipliers and accumulating numbers between them.
📝 Common Number Phrases
🏠 Addresses
One hundred twenty-three Main Street
Five thousand four hundred sixty-two
Twenty-first Avenue
Ordinal numbers in addresses
💰 Finances
Five thousand dollars
Two million investment
One hundred thousand bonus
Written amounts in checks
📅 Dates
Twenty twenty-three
Nineteen ninety-five
Two thousand and one
Year numbers in text
📊 Statistics
Five hundred thousand users
Two billion downloads
One hundred million views
Large numbers in reports
🎯 Precision and Accuracy
✅ Supported Features
All standard English number words
Hyphenated compound numbers
Scale words up to trillion
Complex multi-scale numbers
Ordinal number handling
⚠️ Limitations
Decimal numbers (use separate converter)
Fractions (use separate converter)
Non-English number words
Very large numbers (> trillion)
Regional variations
🔄 Error Handling
Unknown word detection
Invalid format identification
Clear error messages
Suggestion for corrections
Input validation