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Text Normalization Tool

Normalize text for matching — lowercasing, accent removal, whitespace standardization, and more.

1
Upload File
2
Select Fields
3
Configure
4
Transforming
5
Done

Upload a file to transform

Upload a CSV or JSONL file. Fields will be detected automatically.

Drop your file here, or click to browse

.csv or .jsonl · max 10MB

How it works

Text normalization is the first step in any entity resolution pipeline. Before comparing strings, you need to standardize them: convert to lowercase, remove accents (é → e), normalize whitespace, strip punctuation, and handle special characters. This tool shows how normalization transforms your input, which helps you understand why "Müller" and "MUELLER" can be matched after proper normalization.

Use cases in entity resolution

Data cleaning before matching
Input standardization
Cross-system data integration
Search query normalization

Related tools

Levenshtein Distance
Calculate the minimum number of single-character edits (insertions, deletions, substitutions) needed to transform one string into another.
Soundex Phonetic Algorithm
Encode strings by their phonetic sound — names that sound alike produce the same Soundex code.
Cologne Phonetic Algorithm
Phonetic algorithm optimized for German-language names — "Müller" and "Mueller" encode identically.

Use normalization in production

Tilores runs these transformations automatically as part of an entity resolution pipeline — at any scale, without managing infrastructure.

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