Remove Letter Accents

Remove letter accents in the text.

Input

Output

What It Does

The Remove Accents tool strips diacritical marks from accented characters, converting them instantly to their plain ASCII base letter equivalents. Characters like é, à, ñ, ü, ç, and ø are transformed into e, a, n, u, c, and o respectively — a process technically known as Unicode normalization followed by combining mark removal. This conversion is essential in a wide range of programming, web development, and data processing workflows where accented characters create compatibility problems. International text is rich with accented letters that are perfectly valid in modern Unicode-aware software, yet consistently cause headaches in legacy systems, databases with restricted character sets, URL structures, and cross-platform file names. The Remove Accents tool handles this conversion reliably and instantly across dozens of languages, including Spanish, French, German, Portuguese, Polish, Czech, Romanian, and more, covering virtually every Latin-script language that uses diacritics. Web developers rely on it to generate clean URL slugs from article titles containing accented characters — a critical step for building SEO-friendly, browser-compatible URLs. Content managers use it to produce safe file names from multilingual document titles. Data engineers apply it to normalize customer name fields before database lookups or when merging records from different regional datasets. Search systems commonly strip accents during indexing so that a query for 'resume' also surfaces results for 'résumé', giving users the accent-insensitive experience they expect. Beyond purely technical contexts, educators working with romanization, translators preparing text for ASCII-only publishing systems, and developers building label-printing or barcode software all find this tool genuinely valuable. Whether you are managing a multilingual CMS, building an e-commerce platform, or cleaning up an imported CSV of international contacts, the Remove Accents tool eliminates a class of subtle, frustrating encoding errors before they become hard-to-debug production issues.

How It Works

The Remove Letter Accents applies its selected transformation logic to your input and produces output based on the options you choose.

It applies a fixed set of transformation rules to your input, so the output is stable and easy to verify.

All processing happens in your browser, so your input stays on your device during the transformation.

Common Use Cases

Generating SEO-friendly URL slugs from blog post or product titles written in French, Spanish, or Portuguese that contain accented characters browsers and servers may mishandle.
Normalizing customer name and address fields in a CRM or database to enable consistent search, deduplication, and record matching across data collected from different regional offices.
Creating safe, cross-platform file names from document or media titles that include characters like ñ, ü, or ç, which behave unpredictably on certain operating systems and FTP servers.
Preparing text input for legacy software, barcode generators, or APIs that only accept ASCII and silently corrupt or reject Unicode characters containing diacritical marks.
Building a full-text search index where accent-insensitive matching is required, ensuring that searches for 'cafe' and 'café' return the same results without requiring users to type special characters.
Cleaning up CSV or spreadsheet data imported from international sources — such as supplier catalogs or contact lists — before loading the records into a database or analytics pipeline.
Romanizing text for use in label-printing software, shipping systems, or any platform where only the basic Latin alphabet is supported and accented characters cause rendering or processing failures.

How to Use

Paste or type your accented text into the input field — you can enter a single word, a full sentence, a multi-line paragraph, or a large block of CSV-formatted data containing diacritical marks.
Watch the output panel update in real time as the tool processes your text instantly, stripping diacritical marks and displaying the plain ASCII result without requiring you to click a submit button.
Review the converted output to confirm the transformations look correct — for example, checking that 'São Paulo' became 'Sao Paulo', 'über' became 'uber', and 'naïve' became 'naive'.
Click the Copy button to copy the accent-free text to your clipboard, ready to paste directly into a URL field, file name input, database record, or code editor.
For bulk processing tasks, paste large amounts of text at once — the tool handles multi-line input including full paragraphs, numbered lists, and multi-column CSV data without truncation or errors.

Features

Comprehensive diacritic removal covering accented characters from Spanish, French, German, Portuguese, Polish, Czech, Romanian, Turkish, and dozens of other Latin-script languages in a single pass.
Real-time processing that strips accents instantly as you type or paste text, with no submit button required and no delay regardless of input length.
Preserves all non-accented content exactly — standard ASCII letters, numbers, punctuation, whitespace, and line breaks pass through the tool completely unchanged.
Correctly handles both uppercase and lowercase accented variants, converting É to E and é to e while maintaining the original capitalization of base letters throughout the text.
Processes multi-line and bulk text efficiently, making it practical for normalizing entire paragraphs, document sections, or data exports in a single operation.
Produces output that is safe for use in URLs, file names, database fields, HTTP headers, and any environment with strict ASCII or limited character set requirements.
Runs entirely in the browser with no server-side processing, meaning your text never leaves your device — fully private and usable without an account or internet connection after page load.

Examples

Below is a representative input and output so you can see the transformation clearly.

Input

café naïve jalapeño

Output

cafe naive jalapeno

Edge Cases

Very large inputs may take a few seconds to process in the browser. If performance slows, split the input into smaller batches.
Mixed formatting (tabs, line breaks, or inconsistent delimiters) can affect output. Normalize spacing first if needed.
Remove Letter Accents follows the selected options strictly. If the output looks unexpected, re-check option settings and input format.

Troubleshooting

Output looks unchanged: confirm the input contains the pattern this tool modifies and that the correct options are selected.
Output differs from a previous run: confirm that the input and every option match, because deterministic tools should repeat when the settings are identical.
Unexpected characters: check for hidden whitespace or encoding issues in the input and try normalizing first.
Slow processing: reduce input size or try a modern browser with more available memory.

Tips

When using this tool to generate URL slugs, run your text through Remove Accents first to preserve recognizable base letters, then follow up with a Slugify tool to replace spaces with hyphens and remove remaining special characters — using both steps in sequence produces a far more meaningful URL than slugify alone. Remember that accent removal is a one-way, lossy transformation: the original accented spelling cannot be recovered from the ASCII output, so always keep a copy of your source text if you may need the correctly accented version later. For languages like Czech or Polish where diacritics carry meaningful phonetic distinctions, accent-stripped text may look unusual to native speakers, so limit this conversion to technical back-end purposes such as URLs and file names rather than user-facing display content.

Understanding Diacritical Marks and Why Removing Them Still Matters Diacritical marks — commonly called accents — are small symbols attached to letters that indicate a specific pronunciation, stress pattern, or tonal quality. They are fundamental to written language across much of Europe and Latin America. French uses the acute (é), grave (è), circumflex (ê), and diaeresis (ë). German relies on the umlaut (ä, ö, ü). Spanish is recognized by its tilde (ñ) and acute accents on stressed vowels. Portuguese, Polish, Czech, Turkish, Romanian, and Vietnamese each have their own complete sets of diacritics, and in every case those marks are essential to reading and writing those languages accurately. Modern computing handles these characters through Unicode, a universal encoding standard that assigns a unique code point to every character in every human writing system. Unicode means that most contemporary software — web browsers, relational databases, mobile operating systems — can store, display, and transmit accented characters without issue. Despite this progress, the technical world has not uniformly modernized, and accented characters continue to cause real problems in specific, high-frequency contexts. Where Encoding Problems Still Occur Many older systems and internet protocols were architected around ASCII, a 128-character encoding that covers only the basic Latin alphabet, digits, and punctuation — no room for accented letters whatsoever. When Unicode text containing accented characters is passed to an ASCII-only system, results range from garbled replacement characters and question-mark boxes to hard application errors and silent data truncation. HTTP URLs historically restricted characters to a safe ASCII subset, which is why every web framework from Django to Rails to WordPress includes a slug-generation function that strips or replaces accents as a standard preprocessing step. FTP servers, legacy SFTP implementations, certain cloud object storage systems, and ZIP file tools on older operating systems all exhibit similar limitations with non-ASCII file names. Even in modern Unicode-aware systems, accent normalization is a standard preprocessing step in natural language processing and search engineering. A search index that treats 'résumé' and 'resume' as different strings will silently miss relevant documents. Normalizing both the stored content and the query to accent-free text makes search accent-insensitive — a feature end users expect without realizing it requires deliberate engineering effort. The Technical Process: Unicode Normalization Stripping accents is more sophisticated than a simple character substitution table. The robust approach used by modern implementations relies on a two-step Unicode normalization process. In the first step, text is decomposed using NFD — Normalization Form Canonical Decomposition — which splits precomposed characters like é into their constituent parts: the base letter 'e' and a separate combining accent character (U+0301, COMBINING ACUTE ACCENT). In the second step, all characters classified in the Unicode general category 'Mark, Nonspacing' (Mn) — the category that covers combining diacritical marks — are filtered out. What remains are clean base letters without their accents. This NFD approach handles edge cases that a dictionary-based find-and-replace mapping would miss, including less common diacritics and combinations not anticipated by the dictionary author. Accent Removal vs. Transliteration: An Important Distinction It is worth clearly distinguishing between accent removal and full transliteration. Accent removal operates only on Latin-script characters that carry diacritical marks, stripping those marks while leaving base letters intact. Transliteration is a broader operation that converts entire writing systems — Cyrillic to Latin, Arabic to Latin, Greek to Latin — into phonetic Latin equivalents. The Remove Accents tool handles the former use case exclusively. Non-Latin script characters such as Cyrillic, Arabic, Hebrew, or Chinese pass through the tool unchanged. Remove Accents vs. Slugify: Choosing the Right Tool A Slugify tool and a Remove Accents tool are frequently confused because they address overlapping problems. The key difference is scope. Remove Accents performs a single focused operation: diacritic stripping, leaving spacing, capitalization, and punctuation intact. A Slugify tool performs a broader transformation — lowercasing the entire string, replacing whitespace with hyphens, removing punctuation, and often stripping accents as part of the process, though implementations vary in how thoroughly they handle this. Many slugify implementations simply delete unrecognized characters rather than transliterating them, turning 'Café au Lait' into 'caf-au-lait' rather than the more readable 'cafe-au-lait'. Running text through Remove Accents before slugifying ensures that accented characters survive as their base-letter equivalents, producing slugs that remain meaningful and recognizable to human readers and search engines alike.

Frequently Asked Questions

What are diacritical marks, and which accented characters does this tool remove?

Diacritical marks are small symbols — including acute accents (é), grave accents (è), umlauts (ü), tildes (ñ), cedillas (ç), circumflexes (ô), and macrons (ā) — added to base letters to indicate pronunciation or meaning in a given language. The Remove Accents tool covers the full range of combining diacritical marks defined in the Unicode standard for Latin-script languages, including those used in Spanish, French, German, Portuguese, Polish, Czech, Romanian, Turkish, and many others. Both uppercase and lowercase variants are handled correctly, so É converts to E just as é converts to e.

Why would I need to remove accents from text?

The most common reasons involve technical compatibility. Many legacy systems, APIs, and database configurations only accept ASCII characters and will corrupt, truncate, or reject input containing accented Unicode letters. URL generation requires accent-free text to produce slugs that work reliably across all browsers and servers. File names with accented characters can behave unpredictably on certain operating systems, FTP servers, and archiving tools. Search engines and full-text indexes also benefit from normalized, accent-free content so that searches match results regardless of whether users type accented characters.

Does removing accents change the meaning of the text?

Linguistically, yes — accent removal is a lossy transformation. In languages where accents distinguish between words (such as Spanish 'si' meaning 'if' versus 'sí' meaning 'yes', or French 'ou' meaning 'or' versus 'où' meaning 'where'), the stripped text loses those distinctions. For technical purposes like URL slugs, file names, and database keys, this trade-off is universally accepted and expected. Always preserve your original accented text for any user-facing display and use the accent-stripped version only for back-end technical identifiers.

Can the tool process text from multiple languages at the same time?

Yes. Because the tool uses Unicode normalization rather than a language-specific substitution dictionary, it processes all Latin-script diacritical marks simultaneously in a single pass, regardless of which language they originate from. A paragraph mixing French, Spanish, German, and Portuguese accented characters is handled correctly all at once. There is no need to specify the source language or process each language separately.

What is the difference between Remove Accents and a Slugify tool?

These tools address related but distinct problems. Remove Accents performs one focused operation: stripping diacritical marks while leaving spacing, capitalization, and punctuation completely unchanged. A Slugify tool performs a broader URL-preparation transformation — lowercasing the text, replacing spaces with hyphens, removing punctuation, and ideally stripping accents as well. Many slugify implementations handle accented characters poorly, deleting them entirely rather than converting them to base letters. For best results, run text through Remove Accents first to preserve recognizable base letters, then apply a Slugify tool to produce the final URL-safe slug.

Is it safe to use this tool with sensitive or private data?

Yes. The Remove Accents tool runs entirely in your web browser using client-side JavaScript. Your text is never transmitted to a remote server, stored in a database, or logged anywhere. All processing happens locally on your device, which means it is safe to use with personal names, addresses, medical records, or any other sensitive content. You can even use it offline after the page has loaded, since no server communication is required to perform the conversion.