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Browse 11 professional tools

Emoji Picker

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Invisible Character Detector

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List Tools

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Lorem Ipsum Generator

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Markdown Editor & Preview

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Markdown Table Generator

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Text Case Converter

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Text Cleaner

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Text Diff Checker

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URL Slug Generator

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Word Counter

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Text tools cover the things that should be a one-liner in a shell pipeline but feel friction-heavy when you only need them occasionally — convert case, count words for a SEO meta description, diff two configs, detect that invisible character that broke your import. Everything runs locally; useful when you would rather not paste internal documents into a hosted service.

Cleanup & comparison

Whitespace normalization, line-ending fixes (CRLF/LF), dedup with case-insensitive option, and detection of the BOM / ZWSP / NBSP characters that cause "value matches in display but fails == in code" bugs. Text diff uses Myers algorithm (same as git) with character-level highlighting inside changed lines.

Conversion & formatting

Slugify titles to URL paths (handles transliteration for Polish/German/Cyrillic/Greek/CJK), generate Lorem Ipsum and themed alternatives. Markdown preview matches GitHub Flavored Markdown (the de-facto standard); flags syntax that fails on stricter renderers.

Counting & measurement

Word count following UAX #29 boundaries (matches Microsoft Word and Google Docs), reading time at 200-250 wpm, SEO meta-description size hints. For LLM tokens, use the AI Playground — words ≠ tokens, especially for non-English text.

Translation & encoding

3,800+ emoji in Unicode 15.1, with shortcodes (:rocket: style) and the codepoint plus byte counts that matter when storing in databases (MySQL utf8mb4, not utf8).

Frequently asked questions

My text comparison says identical but the files differ. Why?

Almost always invisible characters. Run the input through Invisible Character Detector — common culprits are BOM (UTF-8 byte-order mark at file start), NBSP (non-breaking space copied from PDFs/web pages), and Unicode normalization (NFC vs NFD).

How do I count words the way Google Docs does?

Use the Word Counter — it follows UAX #29 grapheme cluster rules. "23.5" is one word; "well-known" is one word; "don't" is one word. CJK characters count as one word each (per UAX #29), which is why mixed CJK/Latin text counts higher than equivalent reading length.

Why does my generated slug look different in WordPress vs this site?

Different slugify libraries. WordPress uses sanitize_title() with its own transliteration table; this generator uses ICU-style. For specific platform compatibility, slugify in that platform; for general purpose, ASCII-only output works everywhere.

Is plain text the right format for content?

For readable / writeable content where the consumer is a person or a parser that handles text — yes. For structured data (forms, APIs), use JSON/YAML/CSV via the Data Converter. For machine-readable content with embedded markup, markdown is the modern default.

Last updated · E-Utils editorial team