Unicode Normalizer

hipersoft/unicode-normalizer

Normalize text: apply Unicode NFC/NFKC, strip accents and diacritics, transliterate to ASCII and fix garbled mojibake. For clean matching, dedupe and slugs.

Run this scraper →See the API call

Total users

Monthly users

Total runs

Per text normalized

$0.001

Last rebuilt

Oct 26, 2026

Overview

Normalize text at scale — apply Unicode NFC/NFKC, strip accents and diacritics, transliterate to ASCII, and repair garbled "mojibake" text. Clean strings for reliable matching, deduplication, slugs and imports.

Unicode Normalizer input — configure the options in the Apify Console

What it does

Applies Unicode normalization (NFC / NFD / NFKC / NFKD).
Strips accents (café → cafe) and transliterates special letters (ß → ss, æ → ae).
Optionally forces pure ASCII and lowercases.
Fixes mojibake — text where UTF-8 was mis-decoded as Latin-1 (Ã© → é).

Use cases

Data matching / dedupe — normalize names and addresses so variants match.
Slugs & IDs — produce clean ASCII keys from accented text.
Import cleanup — repair garbled characters from legacy exports.

Input

{ "texts": ["Café des Élèves", "Größe"], "form": "NFC", "stripAccents": true, "toAscii": false, "fixMojibake": false }

Field	Type	Description
`texts`	array	Strings to normalize.
`form`	string	`NFC`, `NFD`, `NFKC` or `NFKD`.
`stripAccents`	boolean	Remove diacritics.
`toAscii`	boolean	Transliterate and drop non-ASCII.
`fixMojibake`	boolean	Repair UTF-8-as-Latin-1 garbling.
`lowercase`	boolean	Lowercase the output.

Output

Unicode Normalizer output — the results as a clean dataset

{ "original": "Café des Élèves", "normalized": "Cafe des Eleves", "ascii": "Cafe des Eleves", "changed": true }

Output schema

Field	Type	Description
`original`	string	The input string.
`normalized`	string	Normalized text (per your options).
`ascii`	string	Pure-ASCII version.

FAQ

What's mojibake? Text like Ã© that appears when UTF-8 is read as Latin-1 — enable fixMojibake to repair it.

Can I automate it? Yes — via integrations on the Apify platform and the Apify API.

Notes

Original clean-room implementation.

Input

Field	Type	Description
`texts` required Texts	array	One or more strings to normalize.
`form` Normalization form	string · NFC, NFD, NFKC, NFKD	Unicode normalization form to apply. Default: `"NFC"`
`stripAccents` Strip accents	boolean	Remove diacritics (café → cafe). Default: `true`
`toAscii` Force ASCII	boolean	Transliterate and drop any remaining non-ASCII characters. Default: `false`
`fixMojibake` Fix mojibake	boolean	Repair text where UTF-8 was mis-decoded as Latin-1 (Ã©, â€™). Default: `false`
`lowercase` Lowercase	boolean	Lowercase the normalized output. Default: `false`

API

curl -X POST "https://api.apify.com/v2/acts/hipersoft~unicode-normalizer/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "texts": [
    "Café des Élèves — “Größe”",
    "Ĥéllo Wörld",
    "MojibakeÃ© test"
  ]
}'

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("hipersoft/unicode-normalizer").call(run_input={
    "texts": [
        "Café des Élèves — “Größe”",
        "Ĥéllo Wörld",
        "MojibakeÃ© test"
    ]
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('hipersoft/unicode-normalizer').call({
  "texts": [
    "Café des Élèves — “Größe”",
    "Ĥéllo Wörld",
    "MojibakeÃ© test"
  ]
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Pricing

Event	Price	Paid plans from
Text normalized One text normalized (accents, ASCII, mojibake)	$0.001	$0.0005

You pay only for what the run returns. Set a maximum cost per run and it stops cleanly at that limit.

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