Fuzzy matching
algorithms & tools
Free online calculators for the string matching algorithms used in entity resolution. No signup, no tracking — just paste your strings and compare.
Calculate the minimum number of single-character edits needed to transform one string into another.
Edit distance that adds transpositions to Levenshtein — "teh" and "the" differ by just 1.
The true edit distance with unrestricted transpositions — the most accurate metric for real-world typing errors.
Edit distance based on the Longest Common Subsequence — counts only insertions and deletions.
Measure string similarity with extra weight for matching prefixes — ideal for name matching.
Calculate the Jaro similarity between two strings, commonly used in record linkage.
Measure the cosine of the angle between two strings represented as vectors. Used in RAG and information retrieval.
Compare the overlap between two sets — the intersection divided by the union.
Similarity metric that measures overlap between two samples, related to Jaccard but with different weighting.
Divide strings into substrings of length Q and compare them to determine similarity.
Encode strings by their phonetic sound — "Smith" and "Smyth" produce the same code.
Phonetic algorithm optimized for German-language names. "Müller" and "Mueller" encode identically.
Advanced phonetic encoding that improves on Soundex for English pronunciation patterns.
Normalize text for matching — lowercasing, accent removal, whitespace standardization, and more.
These algorithms power entity resolution
Tilores combines multiple fuzzy matching algorithms with data transformation and rule-based logic to resolve entities at scale. You don't need to implement these yourself — Tilores handles it.