Online Plagiarism Checking Using Data Mining Techniques

The Online Plagiarism Checker Using Data Mining Techniques was developed to provide an efficient and reliable solution for detecting plagiarism in digital documents. The system combines document processing, text extraction, data mining techniques, and similarity analysis methods to identify duplicated content and evaluate document originality. By automating the plagiarism detection process, the proposed solution reduces the time and effort required for manual document comparison while improving the accuracy of content verification. The implementation successfully integrates document fingerprinting, passage-level matching, and OCR-based text extraction to analyze both machine-readable and scanned documents. The system generates detailed plagiarism reports that include similarity percentages and highlighted matching sections, enabling users to understand the extent of content duplication. The web-based architecture further enhances accessibility and usability, making the system suitable for academic institutions, research organizations, and professional environments. The results obtained during testing demonstrate that the proposed approach can effectively detect direct and near-duplicate textual content while maintaining efficient processing performance. The use of data mining techniques contributes to faster document comparison and improved scalability when handling large collections of documents. Overall, the project achieves its objective of providing an automated platform for plagiarism detection and promoting originality in digital content creation.

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