Abstract:

This undergraduate project, “Plagiarism Detection using Natural Language Processing,” focuses on the development of an advanced system to identify and combat plagiarism in textual content. Leveraging Python and web technologies, the project employs Natural Language Processing (NLP) techniques to analyze and compare text, enhancing the accuracy and efficiency of plagiarism detection.

Existing System:

Current plagiarism detection systems often rely on simple matching algorithms, which may produce false positives or miss subtle instances of plagiarism. The need for a more sophisticated approach using NLP techniques is crucial to handle diverse writing styles and paraphrasing.

Proposed System:

The proposed system introduces a novel approach to plagiarism detection by incorporating NLP algorithms. It aims to analyze the semantic meaning of text, identifying similarities beyond literal matches. The system enhances accuracy by considering contextual information, word usage patterns, and sentence structures. By utilizing machine learning models, the system adapts to evolving plagiarism techniques.

System Requirements:

• Python programming language
• Web server (e.g., Flask, Django)
• Database server (e.g., SQLite, PostgreSQL)
• Libraries: NLTK, SpaCy, scikit-learn
• Machine learning models for NLP (e.g., Word Embeddings, LSTM)
• Text corpus for training and validation

Algorithms:

The system employs various NLP algorithms, including cosine similarity for document comparison, TF-IDF (Term Frequency-Inverse Document Frequency) for feature extraction, and machine learning models for identifying patterns indicative of plagiarism. Word embeddings and LSTM networks are used to capture semantic relationships and context in textual content.

Hardware and Software Requirements:

• Hardware: Standard server infrastructure for hosting the web application
• Software: Python, NLP libraries, Web server (e.g., Flask, Django), Database management system, Version control system (e.g., Git)

Architecture:

The system follows a client-server architecture, with a robust backend handling NLP tasks, plagiarism detection logic, and database management. The frontend provides a user-friendly interface for uploading documents, viewing results, and managing plagiarism reports. RESTful APIs facilitate communication between the frontend and backend, ensuring seamless integration.

Technologies Used:

• Python: Core programming language
• NLP libraries (NLTK, SpaCy): Natural Language Processing
• scikit-learn: Machine learning toolkit
• Web server (Flask or Django): Backend web framework
• Database management system (e.g., PostgreSQL): Data storage
• Git: Version control for collaborative development

Web User Interface:

The web interface allows users to upload documents for plagiarism analysis, view detailed reports highlighting potential instances of plagiarism, and access similarity scores. Visualization tools aid in understanding the degree of similarity between documents. The interface is designed for simplicity and accessibility across different devices.

In conclusion, the “Plagiarism Detection using Natural Language Processing” project offers an advanced solution to address the challenges in detecting plagiarism accurately. By integrating NLP techniques and machine learning models, the system aims to provide a robust and effective tool for educators, researchers, and content creators to maintain integrity in textual content.

UML DIAGRAMS

Collaboration Diagram

Architecture diagram

class diagram

sequence diagram

use case diagram

activity diagram

component diagram

Deployment Diagram