Fuzzy Name Match
Fuzzy-match two names with a caller-supplied similarity threshold.
Description
Fuzzy name similarity expresses Levenshtein edit distance relative to the longer name.
Fuzzy name similarity expresses Levenshtein edit distance relative to the longer name. It measures spelling resemblance rather than identity.1
Fuzzy-match two names with a caller-supplied similarity threshold.
When to use Fuzzy Name Match
Use this tool to fuzzy-match two names with a caller-supplied similarity threshold.
Prepare the text, recorded observations, aligned figures or editorial criteria requested. Confirm what each label, count, timestamp or supplied flag represents before interpreting the result.
How to use Fuzzy Name Match and understand its results
- Prepare the text, recorded observations, aligned figures or editorial criteria requested. Confirm what each label, count, timestamp or supplied flag represents before interpreting the result.
- Enter firstName, secondName, matchThresholdPercent using the field table and calculation rules, then run the tool.
- Read editDistance, similarityPercent, matches. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| firstName | First name | string | Required |
| secondName | Second name | string | Required |
| matchThresholdPercent | Match threshold (%) | number; minimum: 0; maximum: 100 | Required |
Trim/lowercase two nonblank names and compare Unicode code points with insertion, deletion and substitution cost one. Enter minimum 0–100. Return distance, percentage and inclusive gate.
similarity%=100(1−distance/max(lengths)); match=similarity≥minimum Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| editDistance | Edit distance | integer; minimum: 0 |
| similarityPercent | Similarity (%) | number; minimum: 0; maximum: 100 |
| matches | Meets supplied threshold | boolean |
{
"input": {
"firstName": "Katherine Johnson",
"secondName": "Kathrine Johnson",
"matchThresholdPercent": 90
},
"output": {
"editDistance": 1,
"similarityPercent": 94.11764705882352,
"matches": true
}
}Limitations and assumptions
No transposition rule, accent stripping or Unicode normalization is applied. Time grows with both name lengths; use short inputs. Similarity cannot verify a person.
Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Extremely large text inputs are rejected. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors. A successfully returned false flag is an evaluated outcome; its meaning is explained above.
Alternative or Complementary methods
Use Transcription Name Match: Compare a transcribed name with a reference using edit distance. For a complementary task, use Back-Translation Similarity: Compare token sets in a source rendering and back-translation without claiming semantic equivalence.
References
-
Fuzzy Name Match — tool.io
Similar or alternative tools
- Transcription Name Match
Compare a transcribed name with a reference using edit distance.
- Back-Translation Similarity
Compare token sets in a source rendering and back-translation without claiming semantic equivalence.