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Category Text Analysis

String Similarity & Diff Engine

Compare text, code, or data with algorithmic precision using visual diffs and statistical distance metrics.

Comparison Algorithm

Pre-processing

Case Sensitive Strip HTML Ignore Whitespace

View Mode

Inline Diff Side-by-Side

Original Text (A)

0 chars | 0 words

Modified Text (B)

0 chars | 0 words

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About

In the digital ecosystem, text uniqueness is a currency. Whether you are a Senior Developer auditing code migrations, an SEO Specialist battling duplicate content penalties, or a Legal Analyst comparing contract revisions, precision is non-negotiable. A simple "match/no-match" result is insufficient for professional workflows.

This String Similarity Checker is an industrial-grade comparison engine designed to quantify textual divergence using mathematically rigorous algorithms. Unlike basic diff tools, we expose the underlying metrics - Levenshtein Edit Distance, Jaccard Index, and Cosine Similarity - allowing you to choose the sensitivity that matches your domain context. From detecting subtle plagiarism to validating JSON configuration files, this tool provides the granular insight required to make data-driven decisions.

Formulas

The core logic of text comparison relies on measuring the "distance" between two sequences. Below are the primary mathematical models used in this engine.

1. Jaccard Index (Set Overlap)
Measures similarity between finite sample sets, defined as the size of the intersection divided by the size of the union:

JA,B=|A∩B||A∪B|=|A∩B||A|+|B|−|A∩B|

2. Levenshtein Distance (Recursive Definition)
The minimum number of single-character edits (insertions, deletions, or substitutions) required to change string a into string b.

levi,j=

{

maxi,j if mini,j=0min

{

levi−1,j+1levi,j−1+1levi−1,j−1+1_{(a_i≠b_j)}

Where 1_{(a_i≠b_j)} is the indicator function equal to 0 when characters match and 1 otherwise.

Reference Data

Algorithm	Best Use Case	Computational Complexity	Sensitivity
Levenshtein Distance	Spell checking, OCR correction, short string matching	O(n×m)	High (Character level)
Jaccard Index	Plagiarism detection, keyword overlap, SEO analysis	O(n+m)	Medium (Set/Token level)
Cosine Similarity	Semantic analysis, document clustering, vector space	O(n)	Low (Structural independent)
Longest Common Subsequence	Diff utilities, version control systems (Git)	O(n×m)	High (Sequence alignment)
Hamming Distance	Binary strings, fixed-length error correction	O(n)	Strict (Fixed length only)

Frequently Asked Questions

Each algorithm measures "similarity" through a different lens. Levenshtein is character-obsessed; it calculates the physical effort to type one string into another, making it ideal for typo detection. Jaccard is token-obsessed; it treats text as a bag of words, ignoring order, which is perfect for checking if two articles cover the same topics. Cosine Similarity treats text as vectors in multi-dimensional space, measuring the angle between them, useful for semantic relevance.

Yes. By enabling the "Strip HTML" pre-processor, the tool will remove all markup tags (<div>, <span>, etc.) before comparison, allowing you to compare the actual content text. For code comparison, disable this feature to ensure syntax characters are included in the diff.

Calculating Levenshtein distance on strings longer than 5,000 characters is computationally expensive (O(N*M) complexity). For extremely large texts (e.g., entire books), switch to the "Jaccard" or "Cosine" algorithms, which are significantly faster and better suited for macro-level comparison.

For Levenshtein, the similarity percentage is derived using the formula: (1 - (Distance / MaxLength)) * 100. This normalizes the raw edit count against the length of the longest string, providing a relative percentage where 100% means identical and 0% means completely disjoint.