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uencoder
PIPELINE Text Processing Beginner 2 steps

Clean text then analyze word statistics

Before analyzing text statistics, cleaning up the input (removing empty lines, trimming whitespace) produces more accurate counts. The first node normalizes the text; the second runs the word-count analysis.

What it solves

Trim and normalize text, then compute word/character/line statistics.

Input → Output

Text Trim & Clean input → Word Count output across 2 steps.

Runs in

1–2 minutes · fully local in your browser.

Before you start

  • A block of text to analyze

The pipeline

  1. 1 Text Trim & Clean text-trim

    Remove empty lines, trim whitespace, collapse spaces. Free, private, 100% client-side.

  2. 2 Word Count word-count

    Count characters, words, lines, sentences, paragraphs, byte size, and estimate reading time. Free, private, and 100% client-side.

Open in Toolchain

Runs locally in your browser. The recipe defines the pipeline only — your input never leaves this tab.

▶Run this workflow

Enter your input below and click Run. All processing happens locally in your browser — nothing is uploaded.

Pipeline progress

  1. Text Trim & Cleantext-trim
  2. Word Countword-count

Sample input

  hello world  
  foo bar

Expected output

Characters: 19
Characters (no spaces): 16
Words: 4
Lines: 2
Sentences: 1
Paragraphs: 1
Bytes (UTF-8): 19
Reading time: ~1 min

This pair is executed against the real tools at build time (tests/unit/recipes.test.ts) — the expected output is verified, not illustrative.

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Learn more

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How to Calculate TF-IDF
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Tools used

Text Trim & Clean
Remove empty lines, trim whitespace, collapse spaces. Free, private, 100% client-side.
TOOL
Word Count
Count characters, words, lines, sentences, paragraphs, byte size, and estimate reading time. Free, private, and 100% client-side.
TOOL