Accent removal is an NLP text normalization technique that removes diacritical marks from characters — converting accented letters (é, ñ, ü) to ASCII equivalents (e, n, u) for search, matching, and standardization.

What Is Accent Removal?

• Definition: Strip diacritics from Unicode text.
• Examples: café → cafe, naïve → naive, Zürich → Zurich.
• Purpose: Normalize text for search and comparison.
• Method: Unicode decomposition + filtering combining characters.
• Also Called: Diacritic stripping, ASCII folding.

Why Accent Removal Matters

• Search: Match "cafe" query to "café" documents.
• Deduplication: Recognize "Muller" and "Müller" as same name.
• URL Slugs: Create ASCII-safe URLs from titles.
• Sorting: Consistent alphabetical ordering.
• Legacy Systems: Compatibility with ASCII-only systems.

Implementation

import unicodedata

def remove_accents(text):
    nfkd = unicodedata.normalize('NFKD', text)
    return ''.join(c for c in nfkd if not unicodedata.combining(c))

remove_accents("café naïve")  # "cafe naive"

Considerations

• Lossy: Different accented letters map to same ASCII.
• Language-specific: Some languages require accents for meaning.
• Search: Often done at index time for both documents and queries.

Accent removal enables robust text matching across languages — essential for multilingual search.