Extract Numbers
From Text

Extract all numbers from any text instantly. Find integers, decimals, percentages, currencies, and scientific notation with intelligent type detection. Perfect for financial analysis, research data processing, document mining.

Extract Every
Number Instantly

Find all numbers in any text with advanced filtering and classification. Extract integers, decimals, percentages, currencies, and scientific notation from documents, reports, and data files.

Whether you're analyzing financial reports, processing research data, or mining information from documents, our number extractor delivers instant, accurate results with comprehensive statistics and export options.

How Number Extraction Works

Simple Steps:

  1. 1Paste your text or load sample data to see extraction in action
  2. 2Choose number type filter - all types, integers, decimals, percentages, currencies, or scientific
  3. 3Enable remove duplicates and select sort order for organized results
  4. 4View extracted numbers in a detailed table with type classification
  5. 5Copy individual numbers or all results at once

Pro Tips:

  • Use the "All Types" filter to see every number in your text at once
  • Enable "Remove Duplicates" when processing data with repeated values
  • Sort by ascending/descending to quickly find min/max values
  • Copy individual numbers for spot checks or all numbers for batch processing
  • Statistics update in real-time and show counts for all number types

Common Use Cases

Financial Report Analysis

Extract revenue figures, costs, and metrics from financial documents and quarterly reports

Example:
Found 47 currency values: $1,234,567.89 revenue, €987,654.32 costs

Scientific Data Mining

Parse research papers and lab reports for measurements, constants, and experimental values

Example:
Extracted 23 scientific notation values including 6.022e23

Survey & Statistics

Pull percentages and response rates from survey results and feedback forms

Example:
Found 15 percentages: 98.2% satisfaction, 15.5% growth rate

Invoice Processing

Extract prices, quantities, and totals from invoices and purchase orders

Example:
Extracted 12 prices, 8 quantities, 3 tax percentages

Data Validation

Verify number counts in documents or check for missing data in reports

Example:
Expected 50 values, found 48 - identified 2 missing entries

Web Scraping Cleanup

Clean and organize numbers from scraped web content and HTML dumps

Example:
Extracted 156 clean numbers from messy scraped data

Frequently Asked Questions

πŸ”§Technical Details & Number Extraction

1Number Types & Detection Patterns

Our extractor uses advanced regular expressions to identify and classify different number formats with high precision. Each number type has a specific pattern that captures its unique characteristics.

Integer Pattern

Regular Expression[+-]?\d{1,3}(?:,\d{3})*(?!\.\d)

Matches whole numbers with optional thousands separators

What It Matches
  • β€’ Simple integers: 42, -15
  • β€’ Formatted numbers: 1,234, 999,999
  • β€’ Large values: 1,234,567,890
Pattern Breakdown
  • β€’ [+-]? - Optional sign
  • β€’ \d{1,3} - 1-3 digits
  • β€’ (?:,\d{3})* - Optional comma groups
  • β€’ (?!\.\d) - Not followed by decimal

Decimal Pattern

Regular Expression[+-]?\d{1,3}(?:,\d{3})*\.\d+

Matches numbers with fractional parts

What It Matches
  • β€’ Simple decimals: 3.14, -2.5
  • β€’ Formatted: 1,234.56
  • β€’ High precision: 3.14159265359
Pattern Breakdown
  • β€’ [+-]? - Optional sign
  • β€’ \d{1,3}(?:,\d{3})* - Integer part
  • β€’ \. - Decimal point (required)
  • β€’ \d+ - One or more decimal digits
πŸ“Š Percentage
\d+(?:\.\d+)?%

Numbers followed by percent sign

βœ“ 50%
βœ“ 99.99%
βœ“ 12.5%
πŸ’° Currency
[$€£Β₯β‚ΉΒ’β‚½β‚©β‚ͺ₦₱ₑ₨฿₺₴₸]\s?\d+(?:\.\d+)?

Currency symbols with values (16+ symbols supported)

βœ“ $1,234.56, €99.99
βœ“ Β₯1000, β‚Ή500
βœ“ β‚©50000, ΰΈΏ5000
πŸ”¬ Scientific
\d+\.?\d*[eE][+-]?\d+

Exponential notation format

βœ“ 1.5e10
βœ“ 3E-5
βœ“ 6.022e23

2Extraction Process & Algorithm

Step-by-step breakdown of how numbers are identified, classified, normalized, and processed from raw text input.

Processing Pipeline

1
Pattern Matching

Scan input text with multiple regex patterns in priority order to identify all potential numbers.

Priority Order: Scientific β†’ Currency β†’ Percentage β†’ Decimal β†’ Integer
2
Type Classification

Determine number type based on format, symbols, and notation.

Classification Rules: Check for e/E notation β†’ currency symbols β†’ % sign β†’ decimal point β†’ default to integer
3
Normalization

Convert matched strings to numeric values by removing formatting characters.

Process: Remove commas β†’ strip currency symbols β†’ remove % β†’ parse float/int
4
Filtering & Deduplication

Apply user-selected type filter and optionally remove duplicate values.

Options: Filter by type (all/specific) β†’ remove duplicates based on numeric value
5
Sorting

Order results by numeric value (ascending/descending) or preserve original text position.

Sort Modes: Original order β†’ Ascending (smallest first) β†’ Descending (largest first)
Example Extraction Process:
Input Text:
"The project costs $1,234.56 (50% of budget) and has 3 phases over 2.5 years."
Extracted Numbers:
1. $1,234.56 β†’ Type: currency, Value: 1234.56
2. 50% β†’ Type: percentage, Value: 50
3. 3 β†’ Type: integer, Value: 3
4. 2.5 β†’ Type: decimal, Value: 2.5

3Normalization & Value Conversion

Understanding how different number formats are converted to standard numeric values for comparison and sorting.

Formatting Removal

Thousands Separators"1,234,567" β†’ 1234567

Remove all commas using replace(/,/g, '')

Currency Symbols"$1,234.56" β†’ 1234.56

Strip symbols: replace(/[$€£Β₯β‚ΉΒ’β‚½β‚©β‚ͺ₦₱ₑ₨฿₺₴₸]/g, '')

Percentage Signs"99.5%" β†’ 99.5

Remove % sign: replace(/%/g, '')

Type Conversion

Integer ParsingparseInt("42", 10) β†’ 42

Base-10 integer conversion

Float ParsingparseFloat("3.14159") β†’ 3.14159

Decimal number conversion

Scientific NotationparseFloat("1.5e10") β†’ 15000000000

Exponential format to standard

Complete Normalization Examples:
Currency Conversion:
"$1,234.56"
β†’ Remove $: "1,234.56"
β†’ Remove comma: "1234.56"
β†’ Parse float: 1234.56
Percentage Conversion:
"99.99%"
β†’ Remove %: "99.99"
β†’ Parse float: 99.99
β†’ Keep original display format
Scientific Notation:
"1.5e10"
β†’ Parse float: 15000000000
β†’ Display: 1.50e+10
β†’ Maintains precision
Large Integer:
"1,234,567,890"
β†’ Remove commas: "1234567890"
β†’ Parse int: 1234567890
β†’ Display: 1.23B

4Deduplication & Sorting Logic

Advanced processing options to organize and filter extracted numbers based on uniqueness and ordering preferences.

Duplicate Removal

How It Works:

Uses Set data structure to track seen numeric values, keeping only the first occurrence of each unique number.

Before: 1, 2, 3, 1, 2, 4
After: 1, 2, 3, 4
Edge Cases:
  • β€’ Different formats, same value: $50 and 50 β†’ Treated as duplicate
  • β€’ Floating point precision: 3.14 and 3.140 β†’ Treated as duplicate
  • β€’ Type doesn't matter: 50% and 50 β†’ Treated as duplicate (same numeric value)

Sort Modes

Original Order

Preserves sequence from source text

Text: "The 3 items cost $50 and $20"
Result: 3, 50, 20
Ascending

Smallest to largest numeric value

Before: 3, 50, 20
After: 3, 20, 50
Descending

Largest to smallest numeric value

Before: 3, 50, 20
After: 50, 20, 3
Combined Processing Example:
Input Text:
"Items: $100, $50, $100, $25, $50"
Original + Duplicates
100, 50, 100, 25, 50
Unique + Ascending
25, 50, 100
Unique + Descending
100, 50, 25

5Statistical Analysis & Aggregation

Comprehensive statistics are calculated from all extracted numbers, providing insights into the data distribution and characteristics.

Calculated Metrics

Count
Total numbers found
By type and overall
Min
Smallest value
Math.min(...values)
Max
Largest value
Math.max(...values)
Sum
Total of all values
reduce((a,b) => a+b)
Mean
Average value
sum / count
Range
Max - Min
Spread of values
Type Distribution

Count of each number type found in the text:

Integerscount of whole numbers
Decimalscount of fractional numbers
Percentagescount of % values
Currenciescount of $€£Β₯β‚Ή values
Scientificcount of e/E notation
Example Calculation
Numbers: 10, 20, 30, 40, 50
Count5
Min10
Max50
Sum150
Mean30
Range40

πŸ”’Privacy & Client-Side Processing

All number extraction and processing happens entirely in your browser. Your text data never leaves your device.

βœ“This Extractor's Privacy

πŸ“Local Processing: All extraction in your browser
πŸ“No Data Transfer: Text never sent to servers
πŸ“Zero Tracking: No analytics on your data
πŸ“Offline Capable: Works without internet
πŸ“No Storage: No cookies or databases

βœ—Server-Based Tools

🚨Server Transmission: Data sent over network
🚨Database Storage: Potentially indefinite retention
🚨Analytics Tracking: Your content monitored
🚨Security Risks: Data breach vulnerabilities
🚨Third-Party Access: Potential data sharing
Technical Implementation:

This extractor uses Vue 3's reactive system with client-side JavaScript for real-time number detection. All regex matching, classification, normalization, and statistics calculations execute in your browser's JavaScript engine without any API calls or data transmission.

Verify Yourself: Open your browser's Network tab (F12 β†’ Network) while using the extractor. You'll see zero HTTP requests related to your text or numbersβ€”proof of complete privacy.

7Performance & Optimization

Advanced techniques used to ensure fast extraction even with large documents and complex number patterns.

Optimization Techniques

Regex Compilation

Patterns compiled once, reused for all matches

Eliminates repeated compilation overhead
Priority Matching

Most specific patterns checked first

Prevents false positives from generic patterns
Early Termination

Stop processing on first match per position

Avoids redundant pattern tests

Performance Characteristics

Small Text (<1KB)Instant

<1ms processing time

Medium Text (1-100KB)Fast

1-10ms processing time

Large Text (>100KB)Quick

10-100ms processing time

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