Remove Accented Characters

This advanced tool converts accented letters to their unaccented base forms using intelligent character mapping. For example, it transforms é→e, ñ→n, ü→u, ç→c, å→a. Unlike simple 'remove non-ASCII' tools that delete characters, this tool replaces them with readable equivalents, preserving text length and readability. It features 3 preset modes: Standard (maps all accents using comprehensive dictionary), Aggressive (uses Unicode NFD normalization for maximum coverage), and Smart (language-aware processing). The tool also offers 8 accent-type controls for granular selection: Grave (àèìòù), Acute (áéíóúý), Circumflex (âêîôû), Tilde (ãñõ), Umlaut/Diaeresis (äëïöüÿ), Cedilla (ç), Ring (å), and Stroke (øđł). Perfect for creating SEO-friendly URLs, database-compatible text, search-friendly content, and ASCII filenames.

The Smart Language Detection feature automatically analyzes your text to identify which language(s) it might be written in, based on accent patterns. It scans for characteristic accented characters: 🇪🇸 Spanish (ñ á é í ó ú), 🇫🇷 French (à è é ê ë î ï ô ù û ü ÿ ç œ æ), 🇩🇪 German (ä ö ü ß), and 🇧🇷 Portuguese (ã õ ç á é í ó ú). The tool displays detected languages with flag emojis and provides a count of accented characters found. For example: '✨ 12 accented chars detected. Possible language: 🇪🇸 Spanish 🇫🇷 French'. This helps you: Verify correct processing - ensure the right characters are being converted, Choose optimal settings - understand which accent types are present, Quality assurance - confirm language-specific handling. If your text contains no accented characters, you'll see '✅ No accented characters found - text is clean.' This immediate feedback saves time and ensures you're processing the right content.

Each preset uses a different accent removal strategy: Standard Mode - Uses a comprehensive 60+ character mapping dictionary that explicitly maps each accented character to its base form (é→e, ñ→n, ß→ss, æ→ae, etc.). This is the most predictable and readable approach, ideal for maintaining text clarity while removing accents. Aggressive Mode - Uses Unicode NFD (Canonical Decomposition) which separates base characters from their combining diacritical marks, then strips the marks. This technique catches accents not in the standard dictionary and is more comprehensive, but can occasionally produce unexpected results with complex characters. Smart Mode - Currently uses the same mapping as Standard but is designed for language-aware processing. In future versions, this may apply language-specific rules (e.g., keeping Spanish ñ while removing other accents). Choose Standard for predictable, dictionary-based conversion with maximum control. Choose Aggressive when you need to catch every possible accent, even rare ones. Choose Smart for language-sensitive processing.

The Accent Type Mode lets you selectively remove specific accent categories while keeping others. The 8 types are: Grave accents (à è ì ò ù) - Common in French and Italian, Acute accents (á é í ó ú ý) - Used in Spanish, Portuguese, French, Circumflex (â ê î ô û) - French indicator of historical 's' or vowel length, Tilde (ã ñ õ) - Spanish 'ñ' is crucial, Portuguese nasal vowels, Umlaut/Diaeresis (ä ë ï ö ü ÿ) - German vowel mutations, French separation, Cedilla (ç) - Makes 'c' soft in French, Portuguese, Turkish, Ring (å) - Swedish, Norwegian, Danish character, Stroke (ø đ ł æ œ ß) - Nordic ø, Polish ł, ligatures, German ß. Use cases: Remove only Grave + Acute for Spanish→English while keeping special characters like ñ (use exceptions). Remove Umlaut for German→English (Müller→Muller). Remove everything except Tilde to keep Spanish ñ. This granular control is perfect when you need to preserve certain language features while normalizing others.

Yes! Use the Character Exceptions feature to specify individual accented characters you want to preserve even when using removal modes. Simply type or paste the exact characters in the exceptions field. Common use cases: Keep Spanish ñ - Enter 'ñ' to preserve this essential Spanish character while removing other accents (España stays España instead of Espana). Keep brand names - Enter 'éü' to preserve Nestlé and Müller in their original form. Keep proper names - Enter specific accents in author names like 'García' or 'Dvořák'. Keep special symbols - Preserve characters like ß (German sharp s) by adding 'ß' to exceptions. The exceptions work across all modes - both presets and accent-type controls. For example, you can use 'Standard' mode with 'ñé' as exceptions, and every accented character will be converted EXCEPT ñ and é. This gives you surgical control over which characters to preserve while still processing everything else. Perfect for maintaining brand consistency while normalizing the rest of your content.

Highlight Changes mode provides visual feedback showing exactly which characters are being converted and how. Each accented character that will be removed appears with: Yellow background - Makes the accented character stand out clearly, Orange text color - High contrast for easy identification, Dotted underline - Additional visual indicator, Tooltip on hover - Shows the exact mapping (e.g., 'é → e', 'ñ → n'). This is incredibly valuable for: Quality Control - Verify the right characters are being converted before finalizing, Learning - Understand which characters are considered accented in different languages, Debugging - Identify unexpected accented characters in your data that you might have missed, Documentation - Show stakeholders or clients exactly what transformations are being applied. The highlighting works in both normal and Comparison views. In Comparison mode, you see original and converted text side-by-side, with the original showing highlights of what will change. This transparency ensures you always understand exactly what's happening to your text before you commit to copying or saving the result.

There are many technical and practical reasons to remove accents: SEO Optimization - Search engines sometimes treat 'café' and 'cafe' as different searches. Unaccented text can improve findability and rank for more keyword variations. URL Generation - Web URLs traditionally use ASCII characters. Converting 'Café München' to 'cafe-munchen' creates clean, shareable URLs that work across all platforms without encoding issues. Filename Creation - Not all file systems handle accents well. ASCII filenames like 'resume.pdf' instead of 'résumé.pdf' avoid cross-platform compatibility problems. Database Compatibility - Some databases have poor accent handling or use case-insensitive collations that don't work well with accents. Search Functionality - Users often search without accents (typing 'cafe' instead of 'café'). Converting text for search indexing improves match rates. Email Addresses - Email systems use ASCII, so user input like 'José García' needs to become 'jose.garcia@domain.com'. Data Normalization - Combining datasets from different sources requires consistent representation. Legacy System Integration - Older systems may not support Unicode properly.

These tools serve different but complementary purposes: Remove Accented Characters - Replaces accented letters with their unaccented equivalents (é→e, ñ→n, ü→u). The text stays readable and maintains roughly the same length. 'Café' becomes 'Cafe' - still makes sense. Best for: SEO, URLs, filenames, search optimization, brand names. Remove Non-ASCII - Deletes all characters outside ASCII range entirely, leaving gaps. 'Café' becomes 'Caf' - loses information. Best for: Legacy database compatibility, pure ASCII requirements, system integration. Key differences: This tool preserves readability by mapping characters intelligently (ß→ss, æ→ae), while Remove Non-ASCII just deletes. This tool has language detection to identify Spanish, French, German, Portuguese patterns. This tool offers accent-type controls for granular selection, while Remove Non-ASCII uses character range controls. Which to choose: Use this tool when you want readable output for humans (SEO, URLs, search). Use Remove Non-ASCII when you need strict ASCII compliance for technical systems. For maximum compatibility, use both in sequence: first remove accents (café→cafe) to preserve readability, then remove remaining non-ASCII to ensure strict compliance.

The tool offers two ways to process multiple items: File Upload - Click 'Upload' to load .txt or .md files directly. The tool reads the file content and processes it immediately. Perfect for large documents, exported data, or prepared content. After processing, use 'Save' to download the clean version with a timestamped filename (e.g., 'no-accents-1642534567.txt'). Batch Mode - When enabled, processes each line of your text independently, preserving line breaks and structure. Essential for: Lists of items - Process 100 names, addresses, or products where each line is separate. CSV data cleaning - Clean comma-separated values while maintaining row structure. Multi-paragraph documents - Ensure each paragraph gets independent processing. Code comments - Process code files line-by-line without affecting structure. Both features work together: upload a file AND enable Batch Mode for line-by-line processing of uploaded content. The Whitespace Normalizer complements these by cleaning up any irregular spacing that might result from character removal, ensuring each line is properly trimmed and formatted. This combination gives you industrial-strength text processing capability for datasets with thousands of entries.

The tool tracks 5 real-time metrics to help you understand and validate the conversion: Input Length - Total characters in original text. Baseline for comparison. Output Length - Characters after conversion. Usually similar to input since we're replacing, not deleting (café→cafe is 4→4 chars). Chars Changed - Count of characters that were actually converted (different from input). Shows the scope of changes. Accents Found - How many accented characters were detected in the original text. Even if some are kept via exceptions, this shows total presence. Changed % - Percentage of characters that were modified, calculated as (changed / input × 100). Indicates the density of accented content. Why these matter: Validation - Confirm the expected number of changes occurred (if you see 0 changes when expecting many, check your settings). Efficiency tracking - See if your text actually needed processing (0% change means it was already clean). Content analysis - Understand how 'international' your text is (20% accented chars suggests heavy foreign language content). Quality metrics - Document the transformation for reports or audits. For example, seeing '47 accents found' but only '12 changed' with exceptions enabled shows your exceptions are working correctly. The statistics update live as you type or change settings, giving instant validation of your configuration.