The algorithm will extract individual words from this text and try to match your noisy word against them.
After you run the algorithm, you'll see how long the search took based on your text and sensitivity.
Lower sensitivity (small threshold) makes the algorithm more strict: it will reject distant matches and usually give more precise suggestions, but it may say "no match" more often. Higher sensitivity is more forgiving and will accept words that are further away, which improves coverage but can reduce accuracy. The runtime mainly grows with text size (more words to compare), while sensitivity changes how the result is interpreted.
Suggestion
No matching word found.
Make sure you have some text and a noisy word before running the algorithm.
Highlighted text preview
Blue chips show the suggested wordI love reading bok on my mobile while learning programming.
How this works
The Levenshtein distance counts the minimum number of single character edits (insertions, deletions, substitutions) needed to turn your noisy word into each candidate word extracted from the text. The smallest distance is selected as the suggestion, as long as it is not greater than your sensitivity threshold.