Word Counter

Count words and characters with language-aware word boundaries.

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What it does

This word counter uses `Intl.Segmenter` when the browser provides it, so word boundaries come from language-aware segmentation instead of a plain split on spaces or punctuation.

That matters for languages such as Chinese, Japanese, and Thai, where meaningful words may not be separated by spaces. The frequency list shows which words appear most often in the text.

Common uses

  • Count words and characters in drafts, snippets, translations, labels, and documentation text.
  • Check non-English text where whitespace-based word counters can undercount or overcount.
  • Inspect the top word frequencies to see repeated terms without scanning the whole text.
  • Leave common-word filtering on when you want rankings to focus on more meaningful terms.

Watch outs

  • Character count uses grapheme clusters when available, so emoji and combined characters are treated like visible characters.
  • Different engines can update language segmentation data over time, but `Intl.Segmenter` is still a better default than splitting on spaces.
  • Word frequencies are case-insensitive, so repeated words with different capitalization are grouped together.
  • Common-word filtering uses a built-in multilingual list. It catches many frequent function words, but it is not exhaustive for every language or domain.

Privacy

Text is counted in your browser. Nothing you type or paste is sent to Crypto Lambda.

FAQ

Why use Intl.Segmenter for word counts?

`Intl.Segmenter` uses language-aware boundaries, so it can count words in text that does not rely on spaces between words.

Are characters counted as bytes?

No. Characters are counted as visible grapheme clusters when supported, not UTF-8 bytes or JavaScript code units.

How are top words ranked?

Words are grouped case-insensitively, sorted by frequency, and capped at the top 100 entries.

Does common-word filtering support every language?

It uses a curated multilingual stop-word list for common function words across several scripts. It helps reduce noise, but no static list can be complete for every language, dialect, or subject area.

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