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Machine Learning · Major Project
Plagiarism Checker with Semantic Similarity
Finds copied and paraphrased passages and highlights the matches
2-3 weeks build
Overview
Compares a submitted document against a reference collection at sentence level. TF-IDF cosine similarity catches near-verbatim copying, while sentence embeddings catch paraphrases that share meaning but not words. Matching passages are highlighted side by side with a similarity score, and a similarity report can be exported per submission.
What makes it stand up
- 01Lexical and semantic similarity combined in one score
- 02Sentence-level matching with side-by-side highlighting
- 03Handles PDF, DOCX and plain-text submissions
- 04Threshold tuning evaluated on a labelled paraphrase set
- 05Exportable similarity report for each document
Module breakdown
- 01
Ingestion
Document parsing and sentence segmentation.
- 02
Lexical match
TF-IDF vectors with cosine similarity.
- 03
Semantic match
Sentence-transformer embeddings and nearest-neighbour search.
- 04
Scoring
Weighted combination with tunable thresholds.
- 05
Report view
React page with matched passages highlighted.
After this, you will be able to
- Explain why embeddings catch paraphrase and TF-IDF does not
- Justify your threshold choice with precision and recall
- Demo detection on a paraphrased paragraph
Technology stack
- Python
- Sentence Transformers
- scikit-learn
- FastAPI
- React
- Level
- Major Project
- Domain
- Machine Learning
- Build time
- 2-3 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support