Deduplication of file names with slight spelling errors involves identifying and eliminating duplicate files even when their names differ minimally due to typos, transposed letters, or variations (e.g., "report_v1.pdf" vs. "repoort_v1.pdf"). It differs from simple exact-match deduplication by using fuzzy matching algorithms that measure similarity, such as Levenshtein distance, to find files that are likely intended to be the same despite minor name discrepancies.

This is particularly useful in environments handling large volumes of user-generated files, such as document management systems in offices, digital asset libraries in creative agencies, or customer uploads on web platforms. Tools like specialized deduplication software, scripting languages (Python libraries like fuzzywuzzy), and some data deduplication solutions can implement this fuzzy logic based on filenames and often metadata.

While this significantly improves organization and storage efficiency by catching otherwise missed duplicates, limitations include computational overhead for large datasets and the risk of false positives (merging genuinely different files with coincidentally similar names). Careful configuration of similarity thresholds is essential to balance thoroughness and accuracy. Future improvements may leverage AI to better understand context and intent behind naming variations.