Text Entropy Calculator

Calculate the Shannon entropy of your text — measuring its information density, randomness, and character frequency distribution.

What Does This Tool Do?

Shannon entropy is a measure from information theory that tells you how "random" or "information-dense" your text is. High entropy means the characters are diverse and unpredictable (like encrypted data or random strings). Low entropy means the text is repetitive or simple (like "aaaaaaa"). This tool calculates the entropy and shows you what it means.

Key Features

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Shannon Entropy

Calculates entropy in bits per character using Shannon's formula.

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Normalized Score

Shows entropy as a percentage of the theoretical maximum for the text.

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Character Frequency

Shows the 5 most frequent characters in your text.

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Plain English Result

Interprets the score as low, medium, or high entropy text.

How to Use This Tool

Paste or type your text.
Click Convert to calculate the entropy.
Read the entropy value and the interpretation.

How It Works

For each unique character, its probability is calculated as (count / total characters). Shannon entropy is then computed as -Σ(p × log₂(p)) summed over all unique characters. The result is in bits per character — the theoretical minimum bits needed to encode each character given this character distribution.

Common Use Cases

💡 Cryptography & Security

Test if encrypted data has sufficiently high entropy (random-looking distribution).

💡 Data Compression

Low-entropy text is highly compressible; high-entropy text is already near optimal.

💡 Text Analysis

Compare entropy across different writing samples, languages, or text types.

💡 Password Strength

High-entropy passwords are harder to crack — measure how random yours is.

Frequently Asked Questions

What is a "good" entropy value?

Natural English text typically has entropy of about 4-5 bits/char. Random data approaches 7-8 bits/char. Simple repetitive text may be 0-2 bits/char. For passwords, aim for 4+ bits/char.

What does normalized entropy mean?

Normalized entropy divides the actual entropy by the maximum possible entropy for the number of unique characters in the text, giving a 0-100% score of how close to maximum randomness the text is.

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