Plagiarism Checker using Flask App in Python Projects

Plagiarism Checker using Flask App in Python Projects
Abstract
The Plagiarism Checker using Flask App Project is a Python-based web application designed to detect content similarity and plagiarism in text documents, academic papers, reports, and online content. The system uses natural language processing (NLP) techniques to compare documents and calculate similarity scores using methods such as cosine similarity, term frequency–inverse document frequency (TF-IDF), word embeddings, and n-gram analysis. The application allows users to upload files or paste text directly through a simple and user-friendly Flask interface, and it returns detailed reports that highlight matched content and similarity percentages. Built using Python libraries like NLTK, Scikit-learn, Pandas, NumPy, and Flask, the system provides fast, accurate, and automated plagiarism checking suitable for students, educational institutions, content creators, and researchers.
Existing System
Existing plagiarism detection systems such as Turnitin, Grammarly, and Copyscape are effective but come with several drawbacks such as high subscription costs, limited accessibility, restrictions for academic users, and lack of customization for specific institutional needs. Many traditional plagiarism tools rely heavily on keyword matching or exact phrase comparison, which fails to detect paraphrased or restructured content. In addition, most commercial solutions are cloud-based and do not allow offline or private document scanning, raising data privacy concerns for sensitive academic or corporate documents. These limitations highlight the need for a customizable and cost-efficient plagiarism detection system.

Proposed System
The proposed system introduces a lightweight, customizable, and privacy-friendly plagiarism detection tool developed with Python and deployed as a web app using Flask. The system uses advanced NLP methods such as TF-IDF vectorization and cosine similarity to accurately detect both direct copy-paste plagiarism and partial similarity from paraphrased content. The Flask interface allows users to upload multiple documents or input raw text, and the backend processes the documents by tokenizing, stemming, and removing stop words to improve accuracy. The result page displays a similarity score along with highlighted matching portions for better clarity. The system can also be extended to include semantic similarity using Word2Vec or BERT embeddings and can integrate document storage or database support for large-scale comparisons. This project provides an effective and educational alternative to commercial tools and demonstrates how plagiarism detection can be implemented using machine learning and NLP in Python.

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