Introduction
Ever wondered how text comparison tools actually detect differences between two documents? Behind the scenes, they rely on powerful algorithms that break down text into patterns, sequences, and edits. In this post, we’ll explore the most widely used algorithms—Longest Common Subsequence (LCS), Levenshtein Distance, and a few advanced techniques—and show how they power tools like onlinetext.compare.
1. Longest Common Subsequence (LCS)
What it does: Finds the longest sequence of characters that appear in both texts in the same order (not necessarily consecutively).
Use case: Ideal for comparing versions of documents or code.
Example: Text A: ABCDEF Text B: ZAYBXCWD → LCS: ABCD
LCS is great for spotting shared structure while ignoring unrelated insertions.
2. Levenshtein Distance
What it does: Measures the minimum number of single-character edits (insertions, deletions, substitutions) needed to transform one string into another.
Use case: Perfect for spelling correction, fuzzy matching, and similarity scoring.
Example: kitten → sitting → Edits: substitute ‘k’→’s’, substitute ‘e’→’i’, insert ‘g’ → Distance: 3
Levenshtein is widely used in autocorrect, search engines, and plagiarism detection.
3. Beyond Basics: Advanced Algorithms
- Damerau-Levenshtein: Adds transposition (swapping adjacent characters) to the mix.
- Jaro-Winkler: Weighs matching characters and their positions—useful for short strings like names.
- Cosine Similarity: Treats text as vectors and compares their angle—used in semantic analysis.
These algorithms offer deeper insights, especially when comparing meaning or structure.
What Next?
Text comparison isn’t just about spotting typos—it’s about understanding how ideas evolve. Whether you’re editing a blog post or reviewing legal documents, these algorithms help you track changes with precision. Tools like onlinetext.compare make these complex methods accessible to everyone.