Confusable Detection¶

Unicode confusables (homoglyphs) are characters from different scripts that look visually identical or very similar. For example, Cyrillic "а" (U+0430) looks like Latin "a" (U+0061). Attackers exploit this for phishing, impersonation, and spoofing.

translit implements Unicode TR39 confusable detection and normalization with multi-target script support, auto-generated from the official Unicode TR39 confusables.txt (version 17.0.0). The tables cover Cyrillic, Greek, Armenian, Georgian, CJK compatibility, mathematical symbols, fullwidth forms, and other visually confusable characters. Mappings are based on visual similarity, not phonetic equivalence.

Detecting confusables¶

from translit import is_confusable, is_mixed_script

# Cyrillic Н looks like Latin H
is_confusable("Неllo")              # => True
is_mixed_script("Неllo")            # => True

# Pure Latin — no confusables
is_confusable("Hello")              # => False
is_mixed_script("Hello")            # => False

Normalizing confusables¶

Replace confusable characters with their target-script equivalents:

from translit import normalize_confusables

# Cyrillic а, е, о → Latin a, e, o
normalize_confusables("Неllo Wоrld")    # => "Hello World"

# Greek omicron → Latin o
normalize_confusables("Ηellο")          # => "Hello"

Target script¶

By default, confusables are normalized to Latin. You can specify a different target script to normalize towards that script instead:

# Normalize to Latin (default) — non-Latin homoglyphs → Latin
normalize_confusables("раypal")                            # → "paypal"

# Normalize to Cyrillic — non-Cyrillic homoglyphs → Cyrillic
normalize_confusables("paypal", target_script="cyrillic")  # → "раураl"

Supported target scripts¶

Target	Mappings	Description
"latin" (default)	~2,063	Non-Latin → Latin. Cyrillic а→a, Greek Ρ→P, etc.
"cyrillic"	~1,369	Non-Cyrillic → Cyrillic. Latin A→А, p→р, etc.

Characters without a confusable equivalent in the target script pass through unchanged. This is pure visual mapping — not transliteration. Latin f has no Cyrillic lookalike, so it stays as f.

Script detection¶

Identify which Unicode scripts are present in a string:

from translit import detect_scripts, Script

scripts = detect_scripts("Hello Мир")
# => [Script.LATIN, Script.CYRILLIC]

scripts = detect_scripts("東京 Tokyo")
# => [Script.HAN, Script.LATIN]

The Script enum¶

Script enumerates the 39 Unicode scripts translit recognizes:

Major world scripts:

Script	Example characters
LATIN	A–Z, a–z, À–ÿ
CYRILLIC	А–Я, а–я
GREEK	Α–Ω, α–ω
ARABIC	ع, ب, ت
HEBREW	א, ב, ג

Indic scripts:

Script	Example characters
DEVANAGARI	अ, आ, इ
BENGALI	অ, আ, ই
GURMUKHI	ਅ, ਆ, ਇ
GUJARATI	અ, આ, ઇ
ORIYA	ଅ, ଆ, ଇ
TAMIL	அ, ஆ, இ
TELUGU	అ, ఆ, ఇ
KANNADA	ಅ, ಆ, ಇ
MALAYALAM	അ, ആ, ഇ
SINHALA	අ, ආ, ඇ

East Asian scripts:

Script	Example characters
HAN	中, 文, 字
HIRAGANA	あ, い, う
KATAKANA	ア, イ, ウ
HANGUL	가, 나, 다

Southeast Asian scripts:

Script	Example characters
THAI	ก, ข, ค
LAO	ກ, ຂ, ຄ
MYANMAR	က, ခ, ဂ
KHMER	ក, ខ, គ
BALINESE	ᬅ, ᬆ, ᬇ
JAVANESE	ꦄ, ꦆ, ꦈ
TAI_LE	ᥐ, ᥑ, ᥒ
NEW_TAI_LUE	ᦀ, ᦁ, ᦂ

Central/North Asian scripts:

Script	Example characters
TIBETAN	ཀ, ཁ, ག
MONGOLIAN	ᠠ, ᠡ, ᠢ

Caucasian scripts:

Script	Example characters
GEORGIAN	ა, ბ, გ
ARMENIAN	Ա, Բ, Գ

African scripts:

Script	Example characters
ETHIOPIC	ሀ, ለ, ሐ
NKO	ߊ, ߋ, ߌ
VAI	ꔀ, ꔁ, ꔂ

Middle Eastern scripts:

Script	Example characters
SYRIAC	ܐ, ܒ, ܓ
THAANA	ހ, ށ, ނ
COPTIC	Ⲁ, Ⲃ, Ⲅ

Americas:

Script	Example characters
CHEROKEE	Ꭰ, Ꭱ, Ꭲ
CANADIAN_ABORIGINAL	ᐁ, ᐂ, ᐃ

Historical European scripts:

Script	Example characters
RUNIC	ᚠ, ᚡ, ᚢ
OGHAM	ᚁ, ᚂ, ᚃ

Meta-scripts:

Script	Description
COMMON	Digits, punctuation, whitespace
INHERITED	Combining diacritical marks

Use cases¶

Anti-phishing¶

Detect domain names that use mixed scripts to impersonate legitimate sites:

from translit import is_mixed_script, normalize_confusables

# Detect Latin homoglyphs in a "Cyrillic" domain
domain = "аpple.com"  # first "a" is Cyrillic
if is_mixed_script(domain):
    normalized = normalize_confusables(domain)
    print(f"Suspicious: looks like {normalized}")

# Detect Cyrillic homoglyphs injected into Russian text
text = "Банк pоссии"  # Latin 'p' and 'o' instead of Cyrillic
normalized = normalize_confusables(text, target_script="cyrillic")
# → "Банк россии" (Latin homoglyphs replaced with Cyrillic)

Username validation¶

Ensure usernames don't contain confusable characters:

from translit import is_confusable

def validate_username(name: str) -> bool:
    if is_confusable(name):
        raise ValueError("Username contains confusable characters")
    return True

Search normalization¶

Normalize confusables before indexing for search:

from translit import TextPipeline

index_pipeline = TextPipeline(
    normalize="NFKC",
    confusables=True,
    fold_case=True,
)