Fuzzy matching is a search technique that finds strings with similarity above a certain threshold rather than requiring exact matches. It handles typos, spelling variations, and abbreviations, widely used in search engines and autocomplete features.

Key algorithms include Levenshtein distance, n-gram similarity, Jaro-Winkler distance, and phonetic similarity (Soundex, Metaphone). Search algorithms books provide systematic coverage.

Elasticsearch's fuzzy query and JavaScript libraries like fuse.js make it easy to implement fuzzy search in web applications.

For character counting, fuzzy matching tolerates minor character count differences (1-2 characters), making it useful when exact character count matching is unnecessary. Information retrieval introduction books provide additional context.