Word Counter

Introduction:

Text statistics describe how writing is structured and how it reads in practice. A word counter and readability checker helps you see length, variety, and pace so you can edit with intent.

Counts and clarity scores arrive together, then frequency lists and ngrams reveal which words carry most weight. Paste text or drop a file, choose options that fit your draft, and read results that are easy to compare.

A short paragraph may show a quick reading time while a dense passage can lift the grade level and lower reading ease. Use consistent settings across drafts so changes reflect your edits rather than different rules.

Results are estimates shaped by language and punctuation, so unusual formatting or very small samples can mislead. When comparing versions, look for direction rather than single point differences.

Technical Details:

The analysis observes words, sentences, characters, whitespace, and syllables in a snapshot of text. From these quantities it derives average word length, average sentence length, lexical density, estimated reading minutes, and estimated speaking minutes. Lexical density is the share of unique words within all words, sometimes called a type–token ratio.

Two readability indices are computed from sentence length and syllables per word. Reading ease increases as sentences shorten and syllables per word fall, while grade level rises with longer sentences or more syllables per word. These indices summarize effort, not writing quality.

Tokens are built from letters with diacritics and internal apostrophes or hyphens, with an option to include numbers. Sentence boundaries follow terminal punctuation. Syllables are estimated with a vowel‑group heuristic and common ending adjustments.

Comparisons are most meaningful within the same language and with the same options for case handling, stop‑words, accent normalization, minimum word length, and n‑gram rules. Extremely short texts can produce noisy indices.

Units, precision, and rounding

• Averages, lexical density, and readability indices use two decimals with a period as the decimal separator.
• Estimated reading minutes assume 200 words/minute; speaking minutes assume 125 words/minute, rounded to the nearest minute with a minimum of one.

Validation and bounds

I/O and tokenization notes

• Input is plain text pasted or read from a local file.
• Tokens include letters with diacritics, apostrophes, and hyphens; optional decimals use “.” or “,”.
• Accent normalization folds characters like “café” to “cafe” before tokenizing.
• Curly apostrophes normalize to straight for counting consistency.

Networking, storage, and charts

• Processing is browser‑based; files are read locally and not uploaded.
• Copy and download actions create CSV and JSON in the session only.
• Charts render from in‑page data; no network requests are made for analysis.

Assumptions and limitations

• Heads‑up Syllable counts use a heuristic and can miscount rare words or names.
• Sentence splitting relies on “.” “!” “?” and may miss abbreviations or ellipses.
• Stop‑words are English‑only and fixed; other languages may skew density.
• Numbers are included by default; toggling them changes counts and averages.
• Very short texts can produce unstable readability scores.
• Accent folding can merge distinct words in some languages.
• N‑grams reflect contiguous tokens only; no stemming or lemmatization is applied.
• Reading and speaking speeds are fixed baselines and do not reflect individual rates.

Edge cases and error sources

• Mixed decimal separators in numbers within the same text.
• Non‑ASCII punctuation or unusual quotes affecting token boundaries.
• Single‑sentence or single‑word inputs yielding division by small counts.
• Long unbroken strings causing wide tokens and skewed histograms.
• Abbreviations with periods increasing sentence counts unexpectedly.
• Hyphenated compounds counted as single tokens rather than two words.
• Grapheme clusters that contain combining marks without normalization.
• Right single quotes and straight apostrophes mixing within contractions.
• Binary or rich‑text files opened as text producing noisy characters.
• Extremely long inputs stressing memory during frequency aggregation.

Scientific and standards context

The reading ease and grade formulas are widely attributed to Rudolf Flesch and to work by J. Peter Kincaid and colleagues on grade level. Type–token ratio is a common lexical diversity measure in linguistics.

Privacy and compliance

No data is transmitted or stored server‑side. Clipboard and downloads operate locally for your session.

Step‑by‑Step Guide:

Text analysis measures counts, readability, and frequent terms to guide revision.

1. Paste text or drop a file into the input area.
2. Open Advanced to set case, numbers, stop‑words, and accents.
3. Choose Minimum word length and N‑gram size.
4. Toggle Across sentences for phrase spans if needed.
5. Review statistics, then scan frequency, n‑grams, and charts.
6. Copy or download CSV or JSON for your records.

Example: After removing stop‑words and setting minimum length to 3, frequent terms shift to content words that better represent the topic.

Use the same settings to compare drafts confidently.

FAQ:

Troubleshooting:

• Numbers dominate results — turn off Include numbers.
• Too many tiny words — increase Minimum word length.
• Phrases look odd — disable Across sentences.
• Mixed character cases — disable Case sensitive.
• Charts seem empty — ensure there is enough text and increase Top N.
• Accents split words — enable Normalize accents.

Glossary:

Token

A unit treated as a word for counting.

Type–token ratio

Unique words divided by total words.

Bigram

Two‑word contiguous sequence.

Trigram

Three‑word contiguous sequence.

Average sentence length

Words per sentence across the text.

Reading ease

Score indicating simplicity of text.

Grade level

Approximate school grade needed to read comfortably.

Stop‑word

Common function word often excluded from analysis.