Mr Calcu | Spot copied content instantly and boost originality with this fast, accurate plagiarism estimator trusted by writers, students, and editors.

Boost credibility and ensure content integrity with our powerful plagiarism score estimator. Quickly detect copied text and gain peace of mind.

Quick Plagiarism Score Estimator

Plagiarism Score Estimator Guidelines

You're just a few clicks away from a cleaner, more original draft.

  • Step 1: Paste your content into the text field provided.
  • Step 2: Click the 'Calculate' button to run the similarity analysis.
  • Step 3: Examine the per-section breakdown and highlighted overlaps.
  • Step 4: Revise sections flagged for high similarity. Rephrase or properly cite them.
  • Step 5: For academic use, double-check that citation style is correctly applied (APA, MLA, etc.).

Plagiarism Score Estimator Description

Understanding Plagiarism Detection

The Plagiarism Score Estimator helps quantify how original a piece of writing is. It compares submitted content with millions of academic and web-based sources using advanced algorithms and natural language processing (NLP).

Key Features:

  • Lexical and syntactic analysis
  • Detection of reworded phrases using semantic embeddings
  • Weighted scoring across document sections
  • Multilingual support for content comparison

Mathematical Foundations

The plagiarism score P is calculated using the following weighted formula:

P = (Σ S_i * W_i) / T
  • Si = Similarity score for the i-th segment (0–1)
  • Wi = Weight of the i-th segment
  • T = Total normalized weight

Similarity Metrics:

  • Cosine Similarity:
    Cosine(A, B) = (A · B) / (||A|| ||B||)
  • Jaccard Index:
    Jaccard(A, B) = |A ∩ B| / |A ∪ B|

How It Works

  1. Text is segmented and tokenized.
  2. Each section is compared with a multilingual database of content.
  3. Matches are identified through exact string and semantic analysis.
  4. Each segment receives a similarity score weighted by relevance and length.
  5. Final output includes plain text, highlights, and a score breakdown.

Real-World Case Studies

Case Study 1: Academic Thesis

  • Initial Score: 35%
  • Reason: Methodology section reused institutional frameworks.
  • After Source Attribution: Score reduced to 12%

Case Study 2: Marketing Blog Post

  • Initial Score: 28%
  • Reason: Copied product descriptions
  • After Rewrite: Score reduced to 5%

Edge Cases Explained

  • Self-plagiarism: Scores may rise when reusing personal work, even with authorship.
  • Standard definitions: Technical phrases may trigger matches; their impact is minimized algorithmically.
  • Paraphrasing: Advanced NLP detects contextual similarity even with different wording.
  • Multilingual support: Translated comparisons can slightly alter accuracy.
  • Quoted material: Quotes should be properly cited to avoid incorrect matches.

Start your scan now and ensure your content is clean, credible, and ready to publish.

Example Calculation

Text SectionSimilarity %WeightWeighted Score
Introduction12%0.20.024
Main Content8%0.50.040
Conclusion15%0.30.045
Total1.00.109 = 10.9%

Frequently Asked Questions

It analyzes your text by comparing it against a large database of sources and calculates a similarity percentage.

Yes, it provides detailed insights into matching sections so you can refine your work and ensure originality.

No, the system downweights universally known facts and definitions to reduce false positives.

Yes, advanced semantic models help identify reworded content, especially when sentence structure remains similar.

Yes, it supports multiple languages. The text is translated and compared against a multilingual corpus.

It indicates a large portion of the text overlaps with known sources. Review flagged sections and revise accordingly.

Longer or more critical sections contribute more to the overall score. See the weighted breakdown in the results.

Generally, a score below 15% is acceptable in academic or professional settings, but always follow your institution's guidelines.

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