advanced-text-search-matching

Fast text search across large documents uses Boyer-Moore exact matching, whitespace normalization, and n-gram fuzzy matching to return precise segment ranges and character offsets for highlighting. It handles transcripts, books, and long articles by indexing document segments first.

Finding quotes with typos or formatting differences is supported through 3-gram fuzzy matching and whitespace/punctuation normalization. This allows reliable search across multi-part documents, returning precise offsets even when the target text has minor edits or variations.

The best way to locate citations across multi-segment transcripts is building an index from document segments, then applying exact substring matching and normalized text search. This approach bounds computation and provides precise start and end offsets for each matched citation.

N-gram fuzzy matching works for highlighting text inside large books by scoring 3-gram similarity to tolerate minor edits. Combined with document indexing and segment mapping, it returns exact start and end offsets required for reliable text highlighting.

Handling whitespace and punctuation variations when searching inside transcripts requires normalized search combined with Boyer-Moore exact matching. This improves resilience to formatting differences while still returning precise character offsets for matched text segments.