Processed locally in your browser — not uploaded
Arabic Search Normalization
Test a practical Arabic search-normalization pipeline against both a user query and stored candidate text. Remove diacritics and Tatweel, normalize Alef and Ya variants, convert Arabic-Indic digits, strip punctuation, and compare exact, phrase, or all-token matches locally in your browser.
How to use
- Enter the search query a user might type and the candidate text stored in your application.
- Choose exact, phrase-containing, or all-token matching.
- Optionally normalize Ta Marbuta when your search requirements favor recall over spelling precision.
- Compare the normalized values and match result before applying the same rules in your search index.
Understanding the output
Both values use the same pipeline: lowercase Latin text, remove Arabic diacritics and Tatweel, normalize Alef and Ya variants, convert Arabic-Indic digits, remove punctuation, and collapse whitespace.
Common issues & tips
- •Normalize query and indexed text with the same rules. Normalizing only the query will still miss inconsistent stored values.
- •Ta Marbuta normalization can increase matches but also create false positives. Leave it off unless your dataset requires it.
- •This demonstrates deterministic text matching, not Arabic stemming, typo tolerance, ranking, or semantic search.
Frequently asked questions
- Should I normalize the query, stored text, or both?
- Both must use the same rules. Normalize a dedicated search field when indexing and apply the identical pipeline to incoming queries.
- Does this include Arabic stemming or typo tolerance?
- No. It demonstrates deterministic character normalization and matching, not stemming, fuzzy search, ranking, or semantic retrieval.
- Should I normalize Ta Marbuta?
- Only when your dataset benefits from the extra recall. Converting ة to ه can match spelling variations but can also create false positives.
- Is my text uploaded?
- No. Normalization and matching run entirely in your browser.
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