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7ba64dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | """Bruitage OCR/typo a longueur constante.
Contrainte forte : les substitutions preservent la longueur du texte, pour que
les offsets gold restent valides sans re-mapping. C'est ce qui permet de scorer
les memes segments propres ET bruites (analyse §5.1, metrique robustesse OCR).
"""
import random
import unicodedata
# substitutions OCR classiques, 1 caractere -> 1 caractere
_OCR_SUBS = {
"0": "O", "O": "0", "o": "0",
"1": "l", "l": "1", "I": "1",
"5": "S", "S": "5",
"8": "B", "B": "8",
"é": "e", "è": "e", "ê": "e", "à": "a", "ç": "c", "û": "u", "ô": "o",
"É": "E", "È": "E", "À": "A", "Ç": "C",
"m": "n", "u": "v",
}
def _strip_accent(ch: str) -> str:
d = unicodedata.normalize("NFD", ch)
base = d[0]
return base if len(base) == 1 else ch
def apply_noise(text: str, rng: random.Random, rate: float = 0.03) -> str:
"""Bruite ~rate des caracteres eligibles. Longueur strictement conservee."""
chars = list(text)
for i, ch in enumerate(chars):
if rng.random() >= rate:
continue
r = rng.random()
if ch in _OCR_SUBS and r < 0.6:
chars[i] = _OCR_SUBS[ch]
elif ch.isalpha() and r < 0.8:
chars[i] = ch.upper() if ch.islower() else ch.lower()
elif ch.isalpha():
chars[i] = _strip_accent(ch)
out = "".join(chars)
assert len(out) == len(text)
return out
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