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@article{209075,
        author = {Sreelal P Surendran and Abhinav P and KM Vignesh and Shivakrishna M and Shruthi D},
        title = {Plagiarism Detection System with Source Identification and Text Highlighting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {560-567},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209075},
        abstract = {Copying text from the web has become effortless, and teachers, journal editors and publishers now receive a steady flow of submissions whose originality is hard to judge by eye. Most checkers return a lone percentage, leaving the reader to guess which lines were borrowed, from where, and whether the borrowing is verbatim or reworded. This paper describes a text plagiarism checker built to answer those three questions. A submitted file (PDF, DOCX or TXT) or pasted text is cleaned and split into sentences with NLTK; every sentence is converted into a semantic vector with Sentence Transformers; and the vectors are matched against a SQLite store of reference documents and against relevant web pages. Sentences whose best match crosses a similarity threshold are credited to the closest source, painted in distinct colors inside the submitted text, and counted toward an overall percentage. The findings are packaged in a downloadable report. The prototype is written in Python and served through Streamlit or Flask. The paper documents the design, the output format, the expected behavior and a plan for evaluation; measured benchmark figures are reserved for future work.},
        keywords = {Plagiarism Detection, Source Identification, Text Highlighting, Natural Language Processing, Sentence Transformers, Semantic Similarity, Report Generation.},
        month = {October},
        }