Text Fingerprint Analyzer
Two texts
Profile similarity
Paste both texts — the comparison needs each side to contain words.
What a fingerprint is — and is not
The features here are counts: sentence lengths, word lengths, lexical diversity, punctuation rates and the share of function words. They describe a text, and they can show that two texts differ in measurable ways.
This is not authorship attribution, plagiarism detection or AI detection. Those need calibrated reference corpora and published error rates, which a browser tool cannot honestly supply. A high similarity simply means the two texts distribute function words and sentence lengths alike — two drafts on the same subject by the same writer will often score high, and so will two texts in the same genre by different writers.
The Text Fingerprint Analyzer builds a measurable profile of one text — sentence lengths, word lengths, diversity, punctuation rates and function-word share — and compares it field by field with a second text.
What is Text Fingerprint Analyzer?
A style is hard to describe and reasonably easy to measure, as long as you measure the right things. The Text Fingerprint Analyzer produces a profile of a text from features that can be counted exactly, then compares two profiles field by field. It is explicit about the narrow question it answers and the questions it cannot.
The profile contains a dozen countable features. Sentence count, mean sentence length and the standard deviation of sentence length describe rhythm. Mean word length, the share of long words and syllables per word describe lexical weight. The type-token ratio, the Guiraud index and the share of words used once describe diversity. The share of function words describes how much of the text is made of closed-class words such as the, of, and, which a writer chooses less consciously than nouns and verbs. Punctuation rates per thousand words cover commas, semicolons, colons, dashes, question marks, exclamation marks, parentheses and quotation marks.
Comparing two texts produces a table of every feature with both values and the difference, plus a single similarity figure. That figure is the cosine similarity of the two function-word distributions, on a scale from zero to one. A high value means the two texts distribute their closed-class vocabulary in similar proportions. In practice two drafts on the same subject by the same writer score high, and so, usually, do two texts in the same genre by different writers. That is not a defect: it is what the measurement means, and the page says so in a band rather than implying certainty.
The tool is deliberately not a forensic instrument. It does not attempt authorship attribution, plagiarism detection or AI detection, because each of those needs calibrated reference corpora and published error rates that a browser tool cannot honestly supply. The temptation to read a similarity score as evidence of shared authorship should be resisted, and the page states the limitation next to the number.
Where it is genuinely useful is revision. Compare an early draft with a later one and the profile shows what changed: shorter sentences, a heavier vocabulary, fewer semicolons, more parentheses. Compare a piece you are proud of with one you are not and the differences are concrete. Compare two passages you suspect are inconsistent — a chapter written months apart, a document assembled from several sources — and the timbre of each becomes visible in the numbers.
Two texts are enough and no sample is required beyond each containing words. Sentence-length statistics need several sentences to be meaningful, and diversity measures need a few hundred tokens, both of which the profile reports so you can judge the weight of each line. Nothing either text contains leaves the page.
None of this requires publishing theory. Stylometry, the quantitative study of writing style, exists in academic research with careful controls and large reference corpora, and the features used here are a small, countable subset of the kind it studies. What an editor actually needs is writing style analysis that is concrete enough to act on: fewer semicolons, shorter sentences, a heavier vocabulary. That is what the profile table gives, and it is honest about the line between describing a text and claiming to identify its author.
How to use Text Fingerprint Analyzer
- Paste the first text into the reference box; its profile appears immediately.
- Paste the second text into the compare box to unlock the field-by-field comparison.
- Read the function-word similarity percentage and its band, then the per-feature table.
- Look for features that differ most — sentence variation, vocabulary weight, punctuation rates — as the concrete changes.
- Read the section on what a fingerprint is not before drawing conclusions from the similarity figure.
When to use Text Fingerprint Analyzer vs related tools
Use the fingerprint analyzer when you want to compare two drafts or two passages on measurable features rather than on impression. When one text alone is the subject and you want its diversity measures explained in detail, the Lexical Diversity Calculator covers that ground.
If the feature you care about is the shape of individual sentences, the Sentence Variety Checker measures the distribution more directly, and the Readability Grade Scorer translates the same counts into established reading-level scores.
Privacy & Security
This tool runs entirely in your browser — no data ever leaves your device. There is no server round-trip, no upload, no logging, and no account required. Your input is processed locally using client-side JavaScript and is never stored, transmitted, or accessible to anyone else. When you close the tab, everything disappears.