Information-theory learner
Reproduce entropy from individual probabilities.
Compare AAAA, ABAB, AABB and AAAB and add the displayed contributions.
Understands zero entropy for one symbol and equal entropy for reordered distributions.
Text Entropy Calculator
Calculate empirical Shannon entropy from scalar, grapheme or UTF-8 byte frequencies. Inspect probabilities and export a report without security claims.
Specialist tool for Text AnalysisLive, copy-ready outputFocused single-task workflowPrivate in your browser
Use this result well
A quick decision brief for this specific tool
Inputs that matter
Text source
Output to expect
Complete JSON report
How it works
A dedicated interactive panel for this data format or operation
- Start with the included valid sample, then replace it with your own input.
- Review errors and compare the output before replacing source data.
Choose your path
Built around the job you need to finish
Measure empirical scalar, grapheme or UTF-8 byte distributions, expose probabilities and Shannon contributions, and separate evenness from unpredictability.
Encoding analyst
Distinguish text-symbol entropy from encoded-byte entropy.
Analyze one emoji as scalars then UTF-8 bytes; inspect byte identities and normalization choices.
Knows the selected observation unit and does not call it password strength.
Mobile dataset reviewer
Keep a complete, inspectable analysis record.
Import a fixture, change policies, review long distributions and export the complete report.
Old results are invalidated and model assumptions travel with the report.
Authoritative checks for this tool
Outputs and checklists are planning aids. Review the linked current authorities and the records, terms, instructions, and requirements that apply to your exact situation before a consequential decision.
- Shannon distribution and evennessNIST Dataplot · Frequency probabilities, distribution entropy and observed-alphabet maximum. This tool uses base-2 logarithms; no randomness or security inference.
- Unicode Text SegmentationUnicode Consortium · Boundary ambiguity and differences between grapheme, word and sentence units.
- Unicode Normalization FormsUnicode Consortium · Optional NFC comparison, without rewriting original evidence.
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Reference & details
How it works
Updated August 2026
How it works
Updated August 2026Observed distribution
For each selected symbol, p=count/N and H=−Σp·log2(p). Base 2 gives bits per symbol. Empty input returns zero by convention.
Observed alphabet
The maximum log2(k) uses observed distinct symbols only. Evenness H/log2(k) is undefined for fewer than two symbol types.
Interpretation limits
Symbol frequencies ignore order and dependence. Evenness and N×H do not estimate passwords, cryptographic randomness, prediction difficulty or actual compression size.
Updated: August 2026
Example Scenarios
Reproduce entropy from individual probabilities. Compare AAAA, ABAB, AABB and AAAB and add the displayed contributions.
Distinguish text-symbol entropy from encoded-byte entropy. Analyze one emoji as scalars then UTF-8 bytes; inspect byte identities and normalization choices.
Keep a complete, inspectable analysis record. Import a fixture, change policies, review long distributions and export the complete report.
Common Mistakes to Avoid
Common Mistakes to Avoid
Calling uniform frequency random
A predictable repeating sequence can have uniform symbol frequency.
Reading N×H as actual compressed size
It is an independent-symbol model quantity, not a compressor or file-format measurement.
FAQ
Both have the same symbol counts. This empirical distribution measure ignores sequence order.
In scalar mode, a sequence containing only one emoji scalar has zero entropy. UTF-8 byte mode is a different distribution.
No. The generator, dependencies, attacker knowledge and guessing model are not measured here.
It is log2(k) for the alphabet observed in this sample, not the maximum of an external alphabet or a generating process.
All frequency rows, probabilities, contributions, settings and limits are included in a JSON report. Preview paging does not truncate it.
About Text Entropy Calculator
Measure empirical scalar, grapheme or UTF-8 byte distributions, expose probabilities and Shannon contributions, and separate evenness from unpredictability.